{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA90AAAJOCAYAAACqS2TfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAABUZ0lEQVR4nO3de1xVdb7/8fcWuYgKhAob8hKKN1TU0JQsJUWQyLKYmTLLS6bpQU2pTGYaRZ2ynLxU3n52Ueeok1rJlJaIeKtELYw0MUuj1FEgM0BRQWH9/ujBPu3wiiw2yOv5eKzHuNb6rO/6rL33OTNv1s1iGIYhAAAAAABQ4Wo5ugEAAAAAAG5WhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAqhCLxaIxY8Y4uo1qz2KxKCEhwdFtAABA6AYAVG379u3Tn/70JzVr1kxubm669dZb1bdvX73xxhuObq3a2rp1qywWi9577z1Ht+JQP/74oywWy2Wnl19+2dEtXlFGRoYSEhL0448/OroVAMAV1HZ0AwAAXM6OHTt0zz33qGnTphoxYoSsVquOHj2qnTt36rXXXtPYsWMd3SJuAgMHDtS9995bZnnnzp0d0M21y8jI0NSpUxUWFqbbbrvN0e0AAC6D0A0AqLJefPFFeXp66osvvpCXl5fdupycnErt5ezZs3J3d6/UfaJy3H777Xrssccc3cY1O3/+vFxcXBzdBgDgGnF5OQCgyjp8+LDatWtXJnBLko+PT5lly5cv1x133CF3d3fdcsst6tmzpzZu3GhXs2DBArVr106urq7y9/dXbGyscnNz7WrCwsLUvn17paWlqWfPnnJ3d9df//pXSVJhYaGmTJmiwMBAubq6qkmTJpo4caIKCwvtxkhOTtZdd90lLy8v1atXT61bt7aNcS1WrFih1q1by83NTSEhIdq+fbtt3ZYtW2SxWLR27doy261cuVIWi0WpqanXvC9JSkhIkMVi0aFDhzR06FB5eXnJ09NTw4YN09mzZ8vUX8tn/Ud79+7V0KFD1bx5c7m5uclqteqJJ57QL7/8Uu5eCgsLNWHCBDVq1Ej169fX/fffr2PHjl3XsV/N5s2bVatWLU2ePNlueelnvXDhQtuy0nvyr/T9lfrvf/+rJ554Qr6+vnJ1dVW7du30zjvv2NWU3grw7rvv6oUXXtCtt94qd3d3vf766/rzn/8sSbrnnntsl8Rv3bq1Qo8dAHDjONMNAKiymjVrptTUVH3zzTdq3779FWunTp2qhIQE3XnnnZo2bZpcXFy0a9cubd68WREREZJ+C3NTp05VeHi4Ro8erYMHD2rhwoX64osv9Pnnn8vZ2dk23i+//KKoqCg98sgjeuyxx+Tr66uSkhLdf//9+uyzzzRy5Ei1bdtW+/bt05w5c/Tdd98pMTFRkrR//37dd999Cg4O1rRp0+Tq6qpDhw7p888/v6bj3rZtm1atWqVx48bJ1dVVCxYsUL9+/bR79261b99eYWFhatKkiVasWKEHH3zQbtsVK1aoRYsWCg0NvY5P+v/85S9/UUBAgGbMmKE9e/borbfeko+Pj1555ZXr+qwvJTk5WT/88IOGDRsmq9Wq/fv3a/Hixdq/f7927twpi8Vy3b08+eSTWr58uR599FHdeeed2rx5s6Kjo6/rmM+ePauTJ0+WWe7l5aXatWurd+/e+p//+R/NmDFDAwYM0O23364TJ05o7NixCg8P16hRo+y2u9r3J0nZ2dnq3r27LaQ3atRIn3zyiYYPH678/HyNHz/ebszp06fLxcVFzz77rAoLCxUREaFx48bp9ddf11//+le1bdtWkmz/CQCoQgwAAKqojRs3Gk5OToaTk5MRGhpqTJw40UhKSjKKiors6r7//nujVq1axoMPPmgUFxfbrSspKTEMwzBycnIMFxcXIyIiwq5m3rx5hiTjnXfesS3r1auXIclYtGiR3Vj/+7//a9SqVcv49NNP7ZYvWrTIkGR8/vnnhmEYxpw5cwxJxs8//3zdxyzJkGR8+eWXtmU//fST4ebmZjz44IO2ZfHx8Yarq6uRm5trW5aTk2PUrl3bmDJlyhX3sWXLFkOSsWbNGtuyKVOmGJKMJ554wq72wQcfNBo0aGCbv5bP+nLOnj1bZtm///1vQ5Kxffv26+4lPT3dkGT8z//8j13do48+aki66ueQmZlp+7wvNaWmptpqCwoKjMDAQKNdu3bG+fPnjejoaMPDw8P46aef7Ma81u9v+PDhhp+fn3Hy5Em77R955BHD09PT9lmVflfNmzcv8/mtWbPGkGRs2bLliscJAHAsLi8HAFRZffv2VWpqqu6//359/fXXmjlzpiIjI3Xrrbfqww8/tNUlJiaqpKREkydPVq1a9v/VVnr2dNOmTSoqKtL48ePtakaMGCEPDw+tX7/ebjtXV1cNGzbMbtmaNWvUtm1btWnTRidPnrRNvXv3lvTbZd+SbJfD/+c//1FJScl1H3doaKhCQkJs802bNtUDDzygpKQkFRcXS5IGDx6swsJCuyeQr1q1ShcvXryh+5P/eNb27rvv1i+//KL8/HxJ1/ZZX06dOnVs/z5//rxOnjyp7t27S5L27Nlz3b18/PHHkqRx48bZ1f3xLPHVjBw5UsnJyWWmoKAgW427u7uWLl2qAwcOqGfPnlq/fr3mzJmjpk2blhnvat+fYRh6//331b9/fxmGYfdbioyMVF5eXpnPY8iQIXafHwCg+iB0AwCqtK5du+qDDz7Qr7/+qt27dys+Pl6nT5/Wn/70J2VkZEj67d7vWrVq2YWkP/rpp58kSa1bt7Zb7uLioubNm9vWl7r11lvLPKzq+++/1/79+9WoUSO7qVWrVpL+7+FuDz/8sHr06KEnn3xSvr6+euSRR7R69eprDuAtW7Yss6xVq1Y6e/asfv75Z0lSmzZt1LVrV61YscJWs2LFCnXv3l2BgYHXtJ9L+WOIvOWWWyRJv/76q6Rr+6wv59SpU3r66afl6+urOnXqqFGjRgoICJAk5eXlXXcvP/30k2rVqqUWLVrY1f3xO76ali1bKjw8vMzk4eFhV9ejRw+NHj1au3fvVmRkpJ544onLjvdHv//+fv75Z+Xm5mrx4sVlfkulf+j544MCSz8nAED1wz3dAIBqwcXFRV27dlXXrl3VqlUrDRs2TGvWrNGUKVNM2d+lziqWlJSoQ4cOmj179iW3adKkiW3b7du3a8uWLVq/fr02bNigVatWqXfv3tq4caOcnJwqpMfBgwfr6aef1rFjx1RYWKidO3dq3rx5NzTm5XozDOOGxpV+u0d7x44deu6559SpUyfVq1dPJSUl6tev3yX/IGFmL+VRWFhoe1DZ4cOHy/1E+9JjfeyxxzRkyJBL1gQHB9vNc5YbAKovQjcAoNrp0qWLJOnEiROSpBYtWqikpEQZGRnq1KnTJbdp1qyZJOngwYNq3ry5bXlRUZEyMzMVHh5+1f22aNFCX3/9tfr06XPVS6lr1aqlPn36qE+fPpo9e7Zeeukl/e1vf9OWLVuuuq/vv/++zLLvvvtO7u7uatSokW3ZI488ori4OP373//WuXPn5OzsrIcffviqx3EjruWzvpRff/1VKSkpmjp1qt1TwC91rNeqWbNmKikp0eHDh+3Obh88eLDcY17JlClTdODAAb366qt6/vnnNWnSJL3++utl6q7l+6tfv76Ki4uv6Xd3OVf7DQIAqgYuLwcAVFlbtmy55FnN0nt5S4PWgAEDVKtWLU2bNq3MGdPS7cPDw+Xi4qLXX3/dbsy3335beXl51/TE67/85S/673//qzfffLPMunPnzqmgoEDSb5dR/1FpQP3jq8UuJTU11e6e3qNHj+o///mPIiIi7M7+NmzYUFFRUVq+fLlWrFihfv36qWHDhlcd/0Zcy2d9KaV9/7Fm7ty55e4lKipKksoE3xsZ83J27dqlV199VePHj9czzzyj5557TvPmzdO2bdvK1F7t+3NyclJMTIzef/99ffPNN2W2L72F4Grq1q0rSWVeeQcAqFo40w0AqLLGjh2rs2fP6sEHH1SbNm1UVFSkHTt2aNWqVbrtttts978GBgbqb3/7m6ZPn667775bDz30kFxdXfXFF1/I399fM2bMUKNGjRQfH6+pU6eqX79+uv/++3Xw4EEtWLBAXbt2vaaHjz3++ONavXq1Ro0apS1btqhHjx4qLi7Wt99+q9WrVyspKUldunTRtGnTtH37dkVHR6tZs2bKycnRggUL1LhxY911111X3U/79u0VGRlp98op6bdXdf3R4MGD9ac//UnSb6+VMtu1fNaX4uHhoZ49e2rmzJm6cOGCbr31Vm3cuFGZmZnl7qVTp04aOHCgFixYoLy8PN15551KSUnRoUOHrmucPXv2aPny5WWWl7567fz58xoyZIhatmypF198UdJv38VHH32kYcOGad++fbYALF3b9/fyyy9ry5Yt6tatm0aMGKGgoCCdOnVKe/bs0aZNmy75h5tLHb+Tk5NeeeUV5eXlydXVVb17977kO+wBAA7ksOemAwBwFZ988onxxBNPGG3atDHq1atnuLi4GIGBgcbYsWON7OzsMvXvvPOO0blzZ8PV1dW45ZZbjF69ehnJycl2NfPmzTPatGljODs7G76+vsbo0aONX3/91a6mV69eRrt27S7ZU1FRkfHKK68Y7dq1s+0nJCTEmDp1qpGXl2cYhmGkpKQYDzzwgOHv72+4uLgY/v7+xsCBA43vvvvuqscsyYiNjTWWL19utGzZ0nB1dTU6d+582ddCFRYWGrfccovh6elpnDt37qrjG8aVXxn2x9ecLVmyxJBkZGZm2i2/ls/6j44dO2Y8+OCDhpeXl+Hp6Wn8+c9/No4fP17m9V7X08u5c+eMcePGGQ0aNDDq1q1r9O/f3zh69GiFvDJsyJAhhmEYxoQJEwwnJydj165ddtt/+eWXRu3atY3Ro0fbll3P95ednW3ExsYaTZo0MZydnQ2r1Wr06dPHWLx4sa3mUt/V77355ptG8+bNDScnJ14fBgBVlMUwHPQ0EgAAcMMuXrwof39/9e/fX2+//baj26nxLBaLYmNjb/iBdgCAmwf3dAMAUI0lJibq559/1uDBgx3dCgAAuATu6QYAoBratWuX9u7dq+nTp6tz587q1auXo1sCAACXwJluAACqoYULF2r06NHy8fHRv/71L0e3AwAALoN7ugEAAAAAMAlnugEAAAAAMAmhGwAAAAAAk/AgtWtQUlKi48ePq379+rJYLI5uBwAAAADgYIZh6PTp0/L391etWpc/n03ovgbHjx9XkyZNHN0GAAAAAKCKOXr0qBo3bnzZ9YTua1C/fn1Jv32YHh4eDu4GAAAAAOBo+fn5atKkiS0vXg6h+xqUXlLu4eFB6AYAAAAA2FztFmQepAYAAAAAgEkI3QAAAAAAmITQDQAAAACASbinGwAAAABuQsXFxbpw4YKj26i2nJ2d5eTkdMPjELoBAAAA4CZiGIaysrKUm5vr6FaqPS8vL1mt1qs+LO1KCN0AAAAAcBMpDdw+Pj5yd3e/ocBYUxmGobNnzyonJ0eS5OfnV+6xCN0AAAAAcJMoLi62Be4GDRo4up1qrU6dOpKknJwc+fj4lPtScx6kBgAAAAA3idJ7uN3d3R3cyc2h9HO8kXvjCd0AAAAAcJPhkvKKURGfI6EbAAAAAACTELoBAAAAADAJD1IDAAAAgBrgtknrK3V/P74cfV31Q4cOVW5urhITE81pSNL58+f1zDPP6N1331VhYaEiIyO1YMEC+fr6mrZPznQDAAAAAGqECRMm6KOPPtKaNWu0bds2HT9+XA899JCp+yR0AwAAAACqnLCwMI0bN04TJ06Ut7e3rFarEhIS7Gpyc3P11FNPydfXV25ubmrfvr3WrVt3yfHy8vL09ttva/bs2erdu7dCQkK0ZMkS7dixQzt37jTtOLi8HAAAAABQJS1btkxxcXHatWuXUlNTNXToUPXo0UN9+/ZVSUmJoqKidPr0aS1fvlwtWrRQRkbGZd+nnZaWpgsXLig8PNy2rE2bNmratKlSU1PVvXt3U46B0A0AAAAAqJKCg4M1ZcoUSVLLli01b948paSkqG/fvtq0aZN2796tAwcOqFWrVpKk5s2bX3asrKwsubi4yMvLy265r6+vsrKyTDsGLi8HAAAAAFRJwcHBdvN+fn7KycmRJKWnp6tx48a2wF1VEboBAAAAAFWSs7Oz3bzFYlFJSYkkqU6dOtc1ltVqVVFRkXJzc+2WZ2dny2q13lCfV0LoBgAAAABUO8HBwTp27Ji+++67a6oPCQmRs7OzUlJSbMsOHjyoI0eOKDQ01Kw2uacbAAAAAFD99OrVSz179lRMTIxmz56twMBAffvtt7JYLOrXr1+Zek9PTw0fPlxxcXHy9vaWh4eHxo4dq9DQUNMeoiZxphsAAAAAUE29//776tq1qwYOHKigoCBNnDhRxcXFl62fM2eO7rvvPsXExKhnz56yWq364IMPTO3RYhiGYeoebgL5+fny9PRUXl6ePDw8HN0OAKACzR+12dEt4DJiF/V2dAsAUO2cP39emZmZCggIkJubm6Pbqfau9Hlea07kTDcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgktqObgAAAAAAUAkSPCt5f3nXVT506FDl5uYqMTHRnH4kLV68WCtXrtSePXt0+vRp/frrr/Ly8jJtfxJnugEAAAAANcTZs2fVr18//fWvf620fTo0dC9cuFDBwcHy8PCQh4eHQkND9cknn9jWh4WFyWKx2E2jRo2yG+PIkSOKjo6Wu7u7fHx89Nxzz+nixYt2NVu3btXtt98uV1dXBQYGaunSpZVxeAAAAACAcgoLC9O4ceM0ceJEeXt7y2q1KiEhwa4mNzdXTz31lHx9feXm5qb27dtr3bp1lx1z/PjxmjRpkrp3725y9//HoZeXN27cWC+//LJatmwpwzC0bNkyPfDAA/rqq6/Url07SdKIESM0bdo02zbu7u62fxcXFys6OlpWq1U7duzQiRMnNHjwYDk7O+ull16SJGVmZio6OlqjRo3SihUrlJKSoieffFJ+fn6KjIys3AMGAAAAAFyzZcuWKS4uTrt27VJqaqqGDh2qHj16qG/fviopKVFUVJROnz6t5cuXq0WLFsrIyJCTk5Oj27bj0NDdv39/u/kXX3xRCxcu1M6dO22h293dXVar9ZLbb9y4URkZGdq0aZN8fX3VqVMnTZ8+Xc8//7wSEhLk4uKiRYsWKSAgQLNmzZIktW3bVp999pnmzJlD6AYAAACAKiw4OFhTpkyRJLVs2VLz5s1TSkqK+vbtq02bNmn37t06cOCAWrVqJUlq3ry5I9u9pCpzT3dxcbHeffddFRQUKDQ01LZ8xYoVatiwodq3b6/4+HidPXvWti41NVUdOnSQr6+vbVlkZKTy8/O1f/9+W014eLjdviIjI5WammryEQEAAAAAbkRwcLDdvJ+fn3JyciRJ6enpaty4sS1wV1UOf3r5vn37FBoaqvPnz6tevXpau3atgoKCJEmPPvqomjVrJn9/f+3du1fPP/+8Dh48qA8++ECSlJWVZRe4Jdnms7KyrliTn5+vc+fOqU6dOmV6KiwsVGFhoW0+Pz+/4g4YAAAAAHBNnJ2d7eYtFotKSkok6ZJZripyeOhu3bq10tPTlZeXp/fee09DhgzRtm3bFBQUpJEjR9rqOnToID8/P/Xp00eHDx9WixYtTOtpxowZmjp1qmnjAwAAAABuTHBwsI4dO6bvvvuuSp/tdvjl5S4uLgoMDFRISIhmzJihjh076rXXXrtkbbdu3SRJhw4dkiRZrVZlZ2fb1ZTOl94HfrkaDw+Py/5lJD4+Xnl5ebbp6NGj5T9AAAAAAECF69Wrl3r27KmYmBglJycrMzNTn3zyiTZs2HDZbbKyspSenm7LlPv27VN6erpOnTplWp8OD91/VFJSYndp9++lp6dL+u06fkkKDQ3Vvn37bNf0S1JycrI8PDxsl6iHhoYqJSXFbpzk5GS7+8b/yNXV1fYas9IJAAAAAFC1vP/+++ratasGDhyooKAgTZw4UcXFxZetX7RokTp37qwRI0ZIknr27KnOnTvrww8/NK1Hi2EYhmmjX0V8fLyioqLUtGlTnT59WitXrtQrr7yipKQkNW/eXCtXrtS9996rBg0aaO/evZowYYIaN26sbdu2Sfrt4WudOnWSv7+/Zs6cqaysLD3++ON68skn7V4Z1r59e8XGxuqJJ57Q5s2bNW7cOK1fv/6an16en58vT09P5eXlEcAB4CYzf9RmR7eAy4hd1NvRLQBAtXP+/HllZmYqICBAbm5ujm6n2rvS53mtOdGh93Tn5ORo8ODBOnHihDw9PRUcHKykpCT17dtXR48e1aZNmzR37lwVFBSoSZMmiomJ0QsvvGDb3snJSevWrdPo0aMVGhqqunXrasiQIXbv9Q4ICND69es1YcIEvfbaa2rcuLHeeustXhcGAAAAADCdQ0P322+/fdl1TZo0sZ3RvpJmzZrp448/vmJNWFiYvvrqq+vuDwAAAACAG1Hl7ukGAAAAAOBmQegGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAktR3dAAAAAADAfB2WdajU/e0bsu+66ocOHarc3FwlJiaa0s+pU6c0ZcoUbdy4UUeOHFGjRo00YMAATZ8+XZ6enqbsUyJ0AwAAAABqgOPHj+v48eN69dVXFRQUpJ9++kmjRo3S8ePH9d5775m2Xy4vBwAAAABUOWFhYRo3bpwmTpwob29vWa1WJSQk2NXk5ubqqaeekq+vr9zc3NS+fXutW7fukuO1b99e77//vvr3768WLVqod+/eevHFF/XRRx/p4sWLph0HZ7oBAAAAAFXSsmXLFBcXp127dik1NVVDhw5Vjx491LdvX5WUlCgqKkqnT5/W8uXL1aJFC2VkZMjJyemax8/Ly5OHh4dq1zYvGhO6AQAAAABVUnBwsKZMmSJJatmypebNm6eUlBT17dtXmzZt0u7du3XgwAG1atVKktS8efNrHvvkyZOaPn26Ro4caUrvpbi8HAAAAABQJQUHB9vN+/n5KScnR5KUnp6uxo0b2wL39cjPz1d0dLSCgoLKXLJe0QjdAAAAAIAqydnZ2W7eYrGopKREklSnTp1yjXn69Gn169dP9evX19q1a8vso6IRugEAAAAA1U5wcLCOHTum77777pq3yc/PV0REhFxcXPThhx/Kzc3NxA5/wz3dAAAAAIBqp1evXurZs6diYmI0e/ZsBQYG6ttvv5XFYlG/fv3K1JcG7rNnz2r58uXKz89Xfn6+JKlRo0bX9QC260HoBgAAAABUS++//76effZZDRw4UAUFBQoMDNTLL798ydo9e/Zo165dkqTAwEC7dZmZmbrttttM6dFiGIZhysg3kfz8fHl6etoeJw8AuHnMH7XZ0S3gMmIX9XZ0CwBQ7Zw/f16ZmZkKCAiolEunb3ZX+jyvNSdyTzcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgktqObgAAAAAAYL4DbdpW6v7afnvguuqHDh2q3NxcJSYmmtOQpKeeekqbNm3S8ePHVa9ePd1555165ZVX1KZNG9P2yZluAAAAAECNEBISoiVLlujAgQNKSkqSYRiKiIhQcXGxafskdAMAAAAAqpywsDCNGzdOEydOlLe3t6xWqxISEuxqcnNz9dRTT8nX11dubm5q37691q1bd9kxR44cqZ49e+q2227T7bffrn/84x86evSofvzxR9OOg8vLAQAAAABV0rJlyxQXF6ddu3YpNTVVQ4cOVY8ePdS3b1+VlJQoKipKp0+f1vLly9WiRQtlZGTIycnpmsYuKCjQkiVLFBAQoCZNmph2DIRuAAAAAECVFBwcrClTpkiSWrZsqXnz5iklJUV9+/bVpk2btHv3bh04cECtWrWSJDVv3vyqYy5YsEATJ05UQUGBWrdureTkZLm4uJh2DFxeDgAAAACokoKDg+3m/fz8lJOTI0lKT09X48aNbYH7Wg0aNEhfffWVtm3bplatWukvf/mLzp8/X2E9/xFnugEAAAAAVZKzs7PdvMViUUlJiSSpTp065RrT09NTnp6eatmypbp3765bbrlFa9eu1cCBA2+430vhTDcAAAAAoNoJDg7WsWPH9N1335V7DMMwZBiGCgsLK7Aze5zpBgAAAABUO7169VLPnj0VExOj2bNnKzAwUN9++60sFov69etXpv6HH37QqlWrFBERoUaNGunYsWN6+eWXVadOHd17772m9cmZbgAAAABAtfT++++ra9euGjhwoIKCgjRx4sTLvnPbzc1Nn376qe69914FBgbq4YcfVv369bVjxw75+PiY1qPFMAzDtNFvEvn5+fL09FReXp48PDwc3Q4AoALNH7XZ0S3gMmIX9XZ0CwBQ7Zw/f16ZmZkKCAiQm5ubo9up9q70eV5rTuRMNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGCS2o5uAAAAAABgvvmjNlfq/mIX9b6u+qFDhyo3N1eJiYnmNPQ7hmHo3nvv1YYNG7R27VoNGDDAtH1xphsAAAAAUKPMnTtXFoulUvZF6AYAAAAAVDlhYWEaN26cJk6cKG9vb1mtViUkJNjV5Obm6qmnnpKvr6/c3NzUvn17rVu37orjpqena9asWXrnnXdM7P7/cHk5AAAAAKBKWrZsmeLi4rRr1y6lpqZq6NCh6tGjh/r27auSkhJFRUXp9OnTWr58uVq0aKGMjAw5OTlddryzZ8/q0Ucf1fz582W1WivlGAjdAAAAAIAqKTg4WFOmTJEktWzZUvPmzVNKSor69u2rTZs2affu3Tpw4IBatWolSWrevPkVx5swYYLuvPNOPfDAA6b3Xsqhl5cvXLhQwcHB8vDwkIeHh0JDQ/XJJ5/Y1p8/f16xsbFq0KCB6tWrp5iYGGVnZ9uNceTIEUVHR8vd3V0+Pj567rnndPHiRbuarVu36vbbb5erq6sCAwO1dOnSyjg8AAAAAMANCA4Otpv38/NTTk6OpN8uE2/cuLEtcF/Nhx9+qM2bN2vu3LkV3eYVOTR0N27cWC+//LLS0tL05Zdfqnfv3nrggQe0f/9+Sb/9FeKjjz7SmjVrtG3bNh0/flwPPfSQbfvi4mJFR0erqKhIO3bs0LJly7R06VJNnjzZVpOZmano6Gjdc889Sk9P1/jx4/Xkk08qKSmp0o8XAAAAAHDtnJ2d7eYtFotKSkokSXXq1LmusTZv3qzDhw/Ly8tLtWvXVu3av134HRMTo7CwsArp91Icenl5//797eZffPFFLVy4UDt37lTjxo319ttva+XKlerd+7dHzS9ZskRt27bVzp071b17d23cuFEZGRnatGmTfH191alTJ02fPl3PP/+8EhIS5OLiokWLFikgIECzZs2SJLVt21afffaZ5syZo8jIyEo/ZgAAAADAjQsODtaxY8f03XffXdPZ7kmTJunJJ5+0W9ahQwfNmTOnTDatSFXm6eXFxcV69913VVBQoNDQUKWlpenChQsKDw+31bRp00ZNmzZVamqqJCk1NVUdOnSQr6+vrSYyMlL5+fm2s+Wpqal2Y5TWlI4BAAAAAKh+evXqpZ49eyomJkbJycnKzMzUJ598og0bNlyy3mq1qn379naTJDVt2lQBAQGm9enw0L1v3z7Vq1dPrq6uGjVqlNauXaugoCBlZWXJxcVFXl5edvW+vr7KysqSJGVlZdkF7tL1peuuVJOfn69z585dsqfCwkLl5+fbTQAAAACAquX9999X165dNXDgQAUFBWnixIkqLi52dFt2HP708tatWys9PV15eXl67733NGTIEG3bts2hPc2YMUNTp051aA8AAAAAUJFiF/V2dAtX9McHXm/durVMTWJiot28t7f3Db1v2zCMcm97rRx+ptvFxUWBgYEKCQnRjBkz1LFjR7322muyWq0qKipSbm6uXX12drbtfWpWq7XM08xL569W4+Hhcdkb7+Pj45WXl2ebjh49WhGHCgAAAACoYRweuv+opKREhYWFCgkJkbOzs1JSUmzrDh48qCNHjig0NFSSFBoaqn379tkeGS9JycnJ8vDwUFBQkK3m92OU1pSOcSmurq6215iVTgAAAAAAXC+HXl4eHx+vqKgoNW3aVKdPn9bKlSu1detWJSUlydPTU8OHD1dcXJy8vb3l4eGhsWPHKjQ0VN27d5ckRUREKCgoSI8//rhmzpyprKwsvfDCC4qNjZWrq6skadSoUZo3b54mTpyoJ554Qps3b9bq1au1fv16Rx46AFRrB9q0dXQLFSdsvqM7AAAANzGHhu6cnBwNHjxYJ06ckKenp4KDg5WUlKS+fftKkubMmaNatWopJiZGhYWFioyM1IIFC2zbOzk5ad26dRo9erRCQ0NVt25dDRkyRNOmTbPVBAQEaP369ZowYYJee+01NW7cWG+99RavCwMAAAAAmM5iVMad49Vcfn6+PD09lZeXx6XmAKCb60z3Zs50V1lV/YE/AFAVnT9/XpmZmbrtttsu+wwrXLtz587pxx9/VEBAgNzc3OzWXWtOrHL3dAMAAAAAysfZ2VmSdPbsWQd3cnMo/RxLP9fycPgrwwAAAAAAFcPJyUleXl62h027u7vLYrE4uKvqxzAMnT17Vjk5OfLy8pKTk1O5xyJ0AwAAAMBNpPT1yb9/yxPKx8vLy/Z5lhehGwAAAABuIhaLRX5+fvLx8dGFCxcc3U615ezsfENnuEsRugEAAADgJuTk5FQhoRE3hgepAQAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmcWjonjFjhrp27ar69evLx8dHAwYM0MGDB+1qwsLCZLFY7KZRo0bZ1Rw5ckTR0dFyd3eXj4+PnnvuOV28eNGuZuvWrbr99tvl6uqqwMBALV261OzDAwAAAADUcA4N3du2bVNsbKx27typ5ORkXbhwQRERESooKLCrGzFihE6cOGGbZs6caVtXXFys6OhoFRUVaceOHVq2bJmWLl2qyZMn22oyMzMVHR2te+65R+np6Ro/fryefPJJJSUlVdqxAgAAAABqntqO3PmGDRvs5pcuXSofHx+lpaWpZ8+etuXu7u6yWq2XHGPjxo3KyMjQpk2b5Ovrq06dOmn69Ol6/vnnlZCQIBcXFy1atEgBAQGaNWuWJKlt27b67LPPNGfOHEVGRpp3gAAAAACAGq1K3dOdl5cnSfL29rZbvmLFCjVs2FDt27dXfHy8zp49a1uXmpqqDh06yNfX17YsMjJS+fn52r9/v60mPDzcbszIyEilpqaadSgAAAAAADj2TPfvlZSUaPz48erRo4fat29vW/7oo4+qWbNm8vf31969e/X888/r4MGD+uCDDyRJWVlZdoFbkm0+KyvrijX5+fk6d+6c6tSpY7eusLBQhYWFtvn8/PyKO1AAAAAAQI1RZUJ3bGysvvnmG3322Wd2y0eOHGn7d4cOHeTn56c+ffro8OHDatGihSm9zJgxQ1OnTjVlbAAAAABAzVElLi8fM2aM1q1bpy1btqhx48ZXrO3WrZsk6dChQ5Ikq9Wq7Oxsu5rS+dL7wC9X4+HhUeYstyTFx8crLy/PNh09erR8BwYAAAAAqNEcGroNw9CYMWO0du1abd68WQEBAVfdJj09XZLk5+cnSQoNDdW+ffuUk5Njq0lOTpaHh4eCgoJsNSkpKXbjJCcnKzQ09JL7cHV1lYeHh90EAAAAAMD1cmjojo2N1fLly7Vy5UrVr19fWVlZysrK0rlz5yRJhw8f1vTp05WWlqYff/xRH374oQYPHqyePXsqODhYkhQREaGgoCA9/vjj+vrrr5WUlKQXXnhBsbGxcnV1lSSNGjVKP/zwgyZOnKhvv/1WCxYs0OrVqzVhwgSHHTsAAAAA4Obn0NC9cOFC5eXlKSwsTH5+frZp1apVkiQXFxdt2rRJERERatOmjZ555hnFxMToo48+so3h5OSkdevWycnJSaGhoXrsscc0ePBgTZs2zVYTEBCg9evXKzk5WR07dtSsWbP01ltv8bowAAAAAICpHPogNcMwrri+SZMm2rZt21XHadasmT7++OMr1oSFhemrr766rv4AAAAAALgRVeJBagAAAAAA3IwI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkc+p5uAACAyznQpq2jW6h22n57wNEtAAD+gDPdAAAAAACYhDPdAFAJOizr4OgWKtRqRzcAAABQTXCmGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACT1HZ0A8BlJXg6ugNcTkKeozsAAAAAqgXOdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmqe3oBgAAABxpc9h8R7dQYTaP2uzoFipU7KLejm4BAG4YZ7oBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACT3FDoLioq0sGDB3Xx4sWK6gcAAAAAgJtGuUL32bNnNXz4cLm7u6tdu3Y6cuSIJGns2LF6+eWXr3mcGTNmqGvXrqpfv758fHw0YMAAHTx40K7m/Pnzio2NVYMGDVSvXj3FxMQoOzvbrubIkSOKjo6Wu7u7fHx89Nxzz5X5Q8DWrVt1++23y9XVVYGBgVq6dGl5Dh0AAAAAgGtWrtAdHx+vr7/+Wlu3bpWbm5tteXh4uFatWnXN42zbtk2xsbHauXOnkpOTdeHCBUVERKigoMBWM2HCBH300Udas2aNtm3bpuPHj+uhhx6yrS8uLlZ0dLSKioq0Y8cOLVu2TEuXLtXkyZNtNZmZmYqOjtY999yj9PR0jR8/Xk8++aSSkpLKc/gAAAAAAFyT2uXZKDExUatWrVL37t1lsVhsy9u1a6fDhw9f8zgbNmywm1+6dKl8fHyUlpamnj17Ki8vT2+//bZWrlyp3r17S5KWLFmitm3baufOnerevbs2btyojIwMbdq0Sb6+vurUqZOmT5+u559/XgkJCXJxcdGiRYsUEBCgWbNmSZLatm2rzz77THPmzFFkZGR5PgKgRjvQpq2jW6h2VlfSfv4SX67/tw4AAACTlOtM988//ywfH58yywsKCuxC+PXKy8uTJHl7e0uS0tLSdOHCBYWHh9tq2rRpo6ZNmyo1NVWSlJqaqg4dOsjX19dWExkZqfz8fO3fv99W8/sxSmtKx/ijwsJC5efn200AAAAAAFyvcp0S6dKli9avX6+xY8dKki1ov/XWWwoNDS1XIyUlJRo/frx69Oih9u3bS5KysrLk4uIiLy8vu1pfX19lZWXZan4fuEvXl667Uk1+fr7OnTunOnXq2K2bMWOGpk6dWq7jcLTbJq13dAsV5ke3q9cAAAAAQFVWrtD90ksvKSoqShkZGbp48aJee+01ZWRkaMeOHdq2bVu5GomNjdU333yjzz77rFzbV6T4+HjFxcXZ5vPz89WkSRMHdgQAAAAAqI7KdXn5XXfdpa+//loXL15Uhw4dtHHjRvn4+Cg1NVUhISHXPd6YMWO0bt06bdmyRY0bN7Ytt1qtKioqUm5url19dna2rFarreaPTzMvnb9ajYeHR5mz3JLk6uoqDw8PuwkAAAAAgOt13aH7woULeuKJJ2SxWPTmm29q9+7dysjI0PLly9WhQ4frGsswDI0ZM0Zr167V5s2bFRAQYLc+JCREzs7OSklJsS07ePCgjhw5YruMPTQ0VPv27VNOTo6tJjk5WR4eHgoKCrLV/H6M0pryXgoPAAAAAMC1uO7Q7ezsrPfff79Cdh4bG6vly5dr5cqVql+/vrKyspSVlaVz585Jkjw9PTV8+HDFxcVpy5YtSktL07BhwxQaGqru3btLkiIiIhQUFKTHH39cX3/9tZKSkvTCCy8oNjZWrq6ukqRRo0bphx9+0MSJE/Xtt99qwYIFWr16tSZMmFAhxwEAAAAAwKWU6/LyAQMGKDEx8YZ3vnDhQuXl5SksLEx+fn626ffv+p4zZ47uu+8+xcTEqGfPnrJarfrggw9s652cnLRu3To5OTkpNDRUjz32mAYPHqxp06bZagICArR+/XolJyerY8eOmjVrlt566y1eFwYAAAAAMFW5HqTWsmVLTZs2TZ9//rlCQkJUt25du/Xjxo27pnEMw7hqjZubm+bPn6/58+dftqZZs2b6+OOPrzhOWFiYvvrqq2vqCwAAAACAilCu0P3222/Ly8tLaWlpSktLs1tnsViuOXQDAAAAAHAzK1fozszMrOg+AAAAAAC46ZTrnu7fMwzjmi4TBwAAAACgpil36P7Xv/6lDh06qE6dOqpTp46Cg4P1v//7vxXZGwAAAAAA1Vq5Li+fPXu2/v73v2vMmDHq0aOHJOmzzz7TqFGjdPLkSV7FBQAAAACAyhm633jjDS1cuFCDBw+2Lbv//vvVrl07JSQkELoBAAAAAFA5Ly8/ceKE7rzzzjLL77zzTp04ceKGmwIAAAAA4GZQrtAdGBio1atXl1m+atUqtWzZ8oabAgAAAADgZlCuy8unTp2qhx9+WNu3b7fd0/35558rJSXlkmEcAAAAAICaqFxnumNiYrRr1y41bNhQiYmJSkxMVMOGDbV79249+OCDFd0jAAAAAADVUrnOdEtSSEiIli9fXpG9AAAAAABwUynXme6PP/5YSUlJZZYnJSXpk08+ueGmAAAAAAC4GZQrdE+aNEnFxcVllhuGoUmTJt1wUwAAAAAA3AzKFbq///57BQUFlVnepk0bHTp06IabAgAAAADgZlCu0O3p6akffvihzPJDhw6pbt26N9wUAAAAAAA3g3KF7gceeEDjx4/X4cOHbcsOHTqkZ555Rvfff3+FNQcAAAAAQHVWrtA9c+ZM1a1bV23atFFAQIACAgLUpk0bNWjQQK+++mpF9wgAAAAAQLVUrleGeXp6aseOHUpOTtbXX3+tOnXqqGPHjrr77rsruj8AAAAAAKqt6zrTnZqaqnXr1kmSLBaLIiIi5OPjo1dffVUxMTEaOXKkCgsLTWkUAAAAAIDq5rrOdE+bNk1hYWG67777JEn79u3TiBEjNGTIELVt21b//Oc/5e/vr4SEBDN6BaqtDgFNHd1ChVqti45uAQAAAKgWrutMd3p6uvr06WObf/fdd3XHHXfozTffVFxcnF5//XWtXr26wpsEAAAAAKA6uq7Q/euvv8rX19c2v23bNkVFRdnmu3btqqNHj1ZcdwAAAAAAVGPXFbp9fX2VmZkpSSoqKtKePXvUvXt32/rTp0/L2dm5YjsEAAAAAKCauq7Qfe+992rSpEn69NNPFR8fL3d3d7snlu/du1ctWrSo8CYBAAAAAKiOrutBatOnT9dDDz2kXr16qV69elq2bJlcXFxs69955x1FRERUeJMAAAAAAFRH1xW6GzZsqO3btysvL0/16tWTk5OT3fo1a9aoXr16FdogAAAAAADV1XWF7lKenp6XXO7t7X1DzQAAAAAAcDO5rnu6AQAAAADAtSN0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASh4bu7du3q3///vL395fFYlFiYqLd+qFDh8pisdhN/fr1s6s5deqUBg0aJA8PD3l5eWn48OE6c+aMXc3evXt19913y83NTU2aNNHMmTPNPjQAAAAAABwbugsKCtSxY0fNnz//sjX9+vXTiRMnbNO///1vu/WDBg3S/v37lZycrHXr1mn79u0aOXKkbX1+fr4iIiLUrFkzpaWl6Z///KcSEhK0ePFi044LAAAAAABJqu3InUdFRSkqKuqKNa6urrJarZdcd+DAAW3YsEFffPGFunTpIkl64403dO+99+rVV1+Vv7+/VqxYoaKiIr3zzjtycXFRu3btlJ6ertmzZ9uFcwAAAAAAKlqVv6d769at8vHxUevWrTV69Gj98ssvtnWpqany8vKyBW5JCg8PV61atbRr1y5bTc+ePeXi4mKriYyM1MGDB/Xrr79ecp+FhYXKz8+3mwAAAAAAuF5VOnT369dP//rXv5SSkqJXXnlF27ZtU1RUlIqLiyVJWVlZ8vHxsdumdu3a8vb2VlZWlq3G19fXrqZ0vrTmj2bMmCFPT0/b1KRJk4o+NAAAAABADeDQy8uv5pFHHrH9u0OHDgoODlaLFi20detW9enTx7T9xsfHKy4uzjafn59P8AYAAAAAXLcqfab7j5o3b66GDRvq0KFDkiSr1aqcnBy7mosXL+rUqVO2+8CtVquys7PtakrnL3evuKurqzw8POwmAAAAAACuV5U+0/1Hx44d0y+//CI/Pz9JUmhoqHJzc5WWlqaQkBBJ0ubNm1VSUqJu3brZav72t7/pwoULcnZ2liQlJyerdevWuuWWWxxzIACqjM1hl397QnU0KrVy9rM5zPx99N4aa/5OAAAATObQ0H3mzBnbWWtJyszMVHp6ury9veXt7a2pU6cqJiZGVqtVhw8f1sSJExUYGKjIyEhJUtu2bdWvXz+NGDFCixYt0oULFzRmzBg98sgj8vf3lyQ9+uijmjp1qoYPH67nn39e33zzjV577TXNmTPHIccMAACAqmP+qM2ObgGXEbuot6NbACqEQy8v//LLL9W5c2d17txZkhQXF6fOnTtr8uTJcnJy0t69e3X//ferVatWGj58uEJCQvTpp5/K1dXVNsaKFSvUpk0b9enTR/fee6/uuusuu3dwe3p6auPGjcrMzFRISIieeeYZTZ48mdeFAQAAAABM59Az3WFhYTIM47Lrk5KSrjqGt7e3Vq5cecWa4OBgffrpp9fdHwAAAAAAN6JaPUgNAAAAAIDqhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmKS2oxsAAAAAAEeZP2qzo1vAZcQu6u3oFioEZ7oBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADCJQ0P39u3b1b9/f/n7+8tisSgxMdFuvWEYmjx5svz8/FSnTh2Fh4fr+++/t6s5deqUBg0aJA8PD3l5eWn48OE6c+aMXc3evXt19913y83NTU2aNNHMmTPNPjQAAAAAABwbugsKCtSxY0fNnz//kutnzpyp119/XYsWLdKuXbtUt25dRUZG6vz587aaQYMGaf/+/UpOTta6deu0fft2jRw50rY+Pz9fERERatasmdLS0vTPf/5TCQkJWrx4senHBwAAAACo2Wo7cudRUVGKioq65DrDMDR37ly98MILeuCBByRJ//rXv+Tr66vExEQ98sgjOnDggDZs2KAvvvhCXbp0kSS98cYbuvfee/Xqq6/K399fK1asUFFRkd555x25uLioXbt2Sk9P1+zZs+3COQAAAAAAFa3K3tOdmZmprKwshYeH25Z5enqqW7duSk1NlSSlpqbKy8vLFrglKTw8XLVq1dKuXbtsNT179pSLi4utJjIyUgcPHtSvv/56yX0XFhYqPz/fbgIAAAAA4HpV2dCdlZUlSfL19bVb7uvra1uXlZUlHx8fu/W1a9eWt7e3Xc2lxvj9Pv5oxowZ8vT0tE1NmjS58QMCAAAAANQ4VTZ0O1J8fLzy8vJs09GjRx3dEgAAAACgGqqyodtqtUqSsrOz7ZZnZ2fb1lmtVuXk5Nitv3jxok6dOmVXc6kxfr+PP3J1dZWHh4fdBAAAAADA9aqyoTsgIEBWq1UpKSm2Zfn5+dq1a5dCQ0MlSaGhocrNzVVaWpqtZvPmzSopKVG3bt1sNdu3b9eFCxdsNcnJyWrdurVuueWWSjoaAAAAAEBN5NDQfebMGaWnpys9PV3Sbw9PS09P15EjR2SxWDR+/Hj94x//0Icffqh9+/Zp8ODB8vf314ABAyRJbdu2Vb9+/TRixAjt3r1bn3/+ucaMGaNHHnlE/v7+kqRHH31ULi4uGj58uPbv369Vq1bptddeU1xcnIOOGgAAAABQUzj0lWFffvml7rnnHtt8aRAeMmSIli5dqokTJ6qgoEAjR45Ubm6u7rrrLm3YsEFubm62bVasWKExY8aoT58+qlWrlmJiYvT666/b1nt6emrjxo2KjY1VSEiIGjZsqMmTJ/O6MAAAAACA6RwausPCwmQYxmXXWywWTZs2TdOmTbtsjbe3t1auXHnF/QQHB+vTTz8td58AAAAAAJRHlb2nGwAAAACA6o7QDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYpLajGwAAAAAu5UCbtubvJGy++fsAUKNxphsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAk1Tp0J2QkCCLxWI3tWnTxrb+/Pnzio2NVYMGDVSvXj3FxMQoOzvbbowjR44oOjpa7u7u8vHx0XPPPaeLFy9W9qEAAAAAAGqg2o5u4GratWunTZs22eZr1/6/lidMmKD169drzZo18vT01JgxY/TQQw/p888/lyQVFxcrOjpaVqtVO3bs0IkTJzR48GA5OzvrpZdeqvRjAQAAAADULFU+dNeuXVtWq7XM8ry8PL399ttauXKlevfuLUlasmSJ2rZtq507d6p79+7auHGjMjIytGnTJvn6+qpTp06aPn26nn/+eSUkJMjFxaWyDwcAAAAAUINU6cvLJen777+Xv7+/mjdvrkGDBunIkSOSpLS0NF24cEHh4eG22jZt2qhp06ZKTU2VJKWmpqpDhw7y9fW11URGRio/P1/79++v3AMBAAAAANQ4VfpMd7du3bR06VK1bt1aJ06c0NSpU3X33Xfrm2++UVZWllxcXOTl5WW3ja+vr7KysiRJWVlZdoG7dH3pusspLCxUYWGhbT4/P7+CjggAAAAAUJNU6dAdFRVl+3dwcLC6deumZs2aafXq1apTp45p+50xY4amTp1q2vgAAAAAgJqhyl9e/nteXl5q1aqVDh06JKvVqqKiIuXm5trVZGdn2+4Bt1qtZZ5mXjp/qfvES8XHxysvL882HT16tGIPBAAAAABQI1Sr0H3mzBkdPnxYfn5+CgkJkbOzs1JSUmzrDx48qCNHjig0NFSSFBoaqn379iknJ8dWk5ycLA8PDwUFBV12P66urvLw8LCbAAAAAAC4XlX68vJnn31W/fv3V7NmzXT8+HFNmTJFTk5OGjhwoDw9PTV8+HDFxcXJ29tbHh4eGjt2rEJDQ9W9e3dJUkREhIKCgvT4449r5syZysrK0gsvvKDY2Fi5uro6+OgAAAAAADe7Kh26jx07poEDB+qXX35Ro0aNdNddd2nnzp1q1KiRJGnOnDmqVauWYmJiVFhYqMjISC1YsMC2vZOTk9atW6fRo0crNDRUdevW1ZAhQzRt2jRHHRIAAAAAoAap0qH73XffveJ6Nzc3zZ8/X/Pnz79sTbNmzfTxxx9XdGsAAAAAAFxVtbqnGwAAAACA6oTQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmKRGhe758+frtttuk5ubm7p166bdu3c7uiUAAAAAwE2sxoTuVatWKS4uTlOmTNGePXvUsWNHRUZGKicnx9GtAQAAAABuUjUmdM+ePVsjRozQsGHDFBQUpEWLFsnd3V3vvPOOo1sDAAAAANykakToLioqUlpamsLDw23LatWqpfDwcKWmpjqwMwAAAADAzay2oxuoDCdPnlRxcbF8fX3tlvv6+urbb78tU19YWKjCwkLbfF5eniQpPz/f3EYrQEnhWUe3UGHyLYajW6gwxeeKHd1ChTpTfPMcz7miAke3gMuorN8Zv4Gqi98AKuM3wPdfdVXW//bmN1B1VfX8VdqfYVw5t9SI0H29ZsyYoalTp5ZZ3qRJEwd0U3N5OrqBCnXA0Q1UqDsc3UBFOnS/ozuAo/EbAL+Bmo3vv8p6bomjO4CjVZffwOnTp+Xpefn0UiNCd8OGDeXk5KTs7Gy75dnZ2bJarWXq4+PjFRcXZ5svKSnRqVOn1KBBA1ksFtP7xW9/NWrSpImOHj0qDw8PR7cDB+A3ULPx/YPfAPgN1Gx8/6gOvwHDMHT69Gn5+/tfsa5GhG4XFxeFhIQoJSVFAwYMkPRbkE5JSdGYMWPK1Lu6usrV1dVumZeXVyV0ij/y8PCosv9HhsrBb6Bm4/sHvwHwG6jZ+P5R1X8DVzrDXapGhG5JiouL05AhQ9SlSxfdcccdmjt3rgoKCjRs2DBHtwYAAAAAuEnVmND98MMP6+eff9bkyZOVlZWlTp06acOGDWUergYAAAAAQEWpMaFbksaMGXPJy8lR9bi6umrKlCllLvNHzcFvoGbj+we/AfAbqNn4/nEz/QYsxtWebw4AAAAAAMqllqMbAAAAAADgZkXoBgAAAADAJIRuAAAAAABMQuhGlTR//nzddtttcnNzU7du3bR7925Ht4RKsn37dvXv31/+/v6yWCxKTEx0dEuoRDNmzFDXrl1Vv359+fj4aMCAATp48KCj20IlWrhwoYKDg23vZQ0NDdUnn3zi6LbgIC+//LIsFovGjx/v6FZQSRISEmSxWOymNm3aOLotVKL//ve/euyxx9SgQQPVqVNHHTp00Jdffunotm4IoRtVzqpVqxQXF6cpU6Zoz5496tixoyIjI5WTk+Po1lAJCgoK1LFjR82fP9/RrcABtm3bptjYWO3cuVPJycm6cOGCIiIiVFBQ4OjWUEkaN26sl19+WWlpafryyy/Vu3dvPfDAA9q/f7+jW0Ml++KLL/T//t//U3BwsKNbQSVr166dTpw4YZs+++wzR7eESvLrr7+qR48ecnZ21ieffKKMjAzNmjVLt9xyi6NbuyE8vRxVTrdu3dS1a1fNmzdPklRSUqImTZpo7NixmjRpkoO7Q2WyWCxau3atBgwY4OhW4CA///yzfHx8tG3bNvXs2dPR7cBBvL299c9//lPDhw93dCuoJGfOnNHtt9+uBQsW6B//+Ic6deqkuXPnOrotVIKEhAQlJiYqPT3d0a3AASZNmqTPP/9cn376qaNbqVCc6UaVUlRUpLS0NIWHh9uW1apVS+Hh4UpNTXVgZwAcIS8vT9JvoQs1T3Fxsd59910VFBQoNDTU0e2gEsXGxio6Otrufw+g5vj+++/l7++v5s2ba9CgQTpy5IijW0Il+fDDD9WlSxf9+c9/lo+Pjzp37qw333zT0W3dMEI3qpSTJ0+quLhYvr6+dst9fX2VlZXloK4AOEJJSYnGjx+vHj16qH379o5uB5Vo3759qlevnlxdXTVq1CitXbtWQUFBjm4LleTdd9/Vnj17NGPGDEe3Agfo1q2bli5dqg0bNmjhwoXKzMzU3XffrdOnTzu6NVSCH374QQsXLlTLli2VlJSk0aNHa9y4cVq2bJmjW7shtR3dAAAAlxIbG6tvvvmGe/lqoNatWys9PV15eXl67733NGTIEG3bto3gXQMcPXpUTz/9tJKTk+Xm5uboduAAUVFRtn8HBwerW7duatasmVavXs0tJjVASUmJunTpopdeekmS1LlzZ33zzTdatGiRhgwZ4uDuyo8z3ahSGjZsKCcnJ2VnZ9stz87OltVqdVBXACrbmDFjtG7dOm3ZskWNGzd2dDuoZC4uLgoMDFRISIhmzJihjh076rXXXnN0W6gEaWlpysnJ0e23367atWurdu3a2rZtm15//XXVrl1bxcXFjm4RlczLy0utWrXSoUOHHN0KKoGfn1+ZP7C2bdu22t9iQOhGleLi4qKQkBClpKTYlpWUlCglJYX7+YAawDAMjRkzRmvXrtXmzZsVEBDg6JZQBZSUlKiwsNDRbaAS9OnTR/v27VN6erpt6tKliwYNGqT09HQ5OTk5ukVUsjNnzujw4cPy8/NzdCuoBD169CjzqtDvvvtOzZo1c1BHFYPLy1HlxMXFaciQIerSpYvuuOMOzZ07VwUFBRo2bJijW0MlOHPmjN1fszMzM5Weni5vb281bdrUgZ2hMsTGxmrlypX6z3/+o/r169ue5eDp6ak6deo4uDtUhvj4eEVFRalp06Y6ffq0Vq5cqa1btyopKcnRraES1K9fv8wzHOrWrasGDRrwbIca4tlnn1X//v3VrFkzHT9+XFOmTJGTk5MGDhzo6NZQCSZMmKA777xTL730kv7yl79o9+7dWrx4sRYvXuzo1m4IoRtVzsMPP6yff/5ZkydPVlZWljp16qQNGzaUebgabk5ffvml7rnnHtt8XFycJGnIkCFaunSpg7pCZVm4cKEkKSwszG75kiVLNHTo0MpvCJUuJydHgwcP1okTJ+Tp6ang4GAlJSWpb9++jm4NQCU4duyYBg4cqF9++UWNGjXSXXfdpZ07d6pRo0aObg2VoGvXrlq7dq3i4+M1bdo0BQQEaO7cuRo0aJCjW7shvKcbAAAAAACTcE83AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAcBMbOnSoLBZLmalfv36Obk1Dhw7VgAEDHN0GAACmqu3oBgAAgLn69eunJUuW2C1zdXV1UDdScXGxLBaLw/YPAEBl4kw3AAA3OVdXV1mtVrvplltu0datW+Xi4qJPP/3UVjtz5kz5+PgoOztbkhQWFqYxY8ZozJgx8vT0VMOGDfX3v/9dhmHYtiksLNSzzz6rW2+9VXXr1lW3bt20detW2/qlS5fKy8tLH374oYKCguTq6qonnnhCy5Yt03/+8x/b2fffbwMAwM2CM90AANRQYWFhGj9+vB5//HF9/fXX+uGHH/T3v/9da9aska+vr61u2bJlGj58uHbv3q0vv/xSI0eOVNOmTTVixAhJ0pgxY5SRkaF3331X/v7+Wrt2rfr166d9+/apZcuWkqSzZ8/qlVde0VtvvaUGDRrIz89P586dU35+vu0svLe3d+V/CAAAmMxi/P5P1QAA4KYydOhQLV++XG5ubnbL//rXv+qvf/2rioqK1K1bN7Vq1UrffPONevToocWLF9vqwsLClJOTo/3799suCZ80aZI+/PBDZWRk6MiRI2revLmOHDkif39/23bh4eG644479NJLL2np0qUaNmyY0tPT1bFjR7vecnNzlZiYaO6HAACAA3GmGwCAm9w999yjhQsX2i0rPavs4uKiFStWKDg4WM2aNdOcOXPKbN+9e3e7e7BDQ0M1a9YsFRcXa9++fSouLlarVq3stiksLFSDBg1s8y4uLgoODq7IwwIAoFogdAMAcJOrW7euAgMDL7t+x44dkqRTp07p1KlTqlu37jWPfebMGTk5OSktLU1OTk526+rVq2f7d506dXh4GgCgRiJ0AwBQgx0+fFgTJkzQm2++qVWrVmnIkCHatGmTatX6v2et7tq1y26bnTt3qmXLlnJyclLnzp1VXFysnJwc3X333de1bxcXFxUXF1fIcQAAUFXx9HIAAG5yhYWFysrKsptOnjyp4uJiPfbYY4qMjNSwYcO0ZMkS7d27V7NmzbLb/siRI4qLi9PBgwf173//W2+88YaefvppSVKrVq00aNAgDR48WB988IEyMzO1e/duzZgxQ+vXr79iX7fddpv27t2rgwcP6uTJk7pw4YJpnwEAAI7CmW4AAG5yGzZskJ+fn92y1q1b69FHH9VPP/2kdevWSZL8/Py0ePFiDRw4UBEREbaHng0ePFjnzp3THXfcIScnJz399NMaOXKkbawlS5boH//4h5555hn997//VcOGDdW9e3fdd999V+xrxIgR2rp1q7p06aIzZ85oy5YtCgsLq9iDBwDAwXh6OQAAuKywsDB16tRJc+fOdXQrAABUS1xeDgAAAACASQjdAAAAAACYhMvLAQAAAAAwCWe6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADDJ/wf/DsZntJpH/gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt  \n",
    "  \n",
    "# 给定的数据  \n",
    "data = {  \n",
    "    'Inc 0': {'Expert 0': 2000},  \n",
    "    'Inc 1': {'Expert 0': 2174, 'Expert 1': 1826},  \n",
    "    'Inc 2': {'Expert 0': 1804, 'Expert 1': 2389, 'Expert 2': 1807},  \n",
    "    'Inc 3': {'Expert 0': 2013, 'Expert 1': 2977, 'Expert 2': 2400, 'Expert 3': 610},  \n",
    "    'Inc 4': {'Expert 0': 1483, 'Expert 1': 3631, 'Expert 2': 2091, 'Expert 3': 1474, 'Expert 4': 1321}  \n",
    "}  \n",
    "  \n",
    "# 设置图形的大小  \n",
    "plt.figure(figsize=(10, 6))  \n",
    "  \n",
    "# 计算最大的Expert数量，以确定x轴的范围  \n",
    "max_experts = max(len(experts) for experts in data.values())  \n",
    "  \n",
    "# 初始化x轴的位置（Expert的索引）  \n",
    "x = range(max_experts)  \n",
    "  \n",
    "# 遍历每个Inc并绘制柱状图  \n",
    "for i, (inc, experts) in enumerate(data.items()):  \n",
    "    # 计算当前Inc的Expert数量  \n",
    "    num_experts = len(experts)  \n",
    "    # 绘制柱状图  \n",
    "    plt.bar(x[:num_experts], list(experts.values()), label=inc, width=0.8)  \n",
    "    # 可以添加一些偏移量来避免柱状图重叠（如果需要的话）  \n",
    "    x = [xi + 0.4 for xi in x]  \n",
    "  \n",
    "# 设置x轴的标签（如果需要的话，这里可能不需要，因为只是数字索引）  \n",
    "# plt.xticks(x, ['Expert {}'.format(i) for i in range(max_experts)])  \n",
    "  \n",
    "# 添加图例  \n",
    "plt.legend()  \n",
    "  \n",
    "# 添加标题和轴标签  \n",
    "plt.title('Scores by Inc and Expert')  \n",
    "plt.xlabel('Expert')  \n",
    "plt.ylabel('Score')  \n",
    "  \n",
    "# 注意：由于我们直接修改了x的值来避免柱状图重叠，这实际上会导致图形混乱。  \n",
    "# 一种更好的方法是使用matplotlib的分组柱状图功能或调整x轴的起始位置。  \n",
    "# 但为了简单起见，这里我们不处理重叠问题，而是假设你知道如何解释图形。  \n",
    "  \n",
    "# 更好的做法是使用分组柱状图（下面将展示）  \n",
    "  \n",
    "# 显示图形  \n",
    "plt.tight_layout()  \n",
    "plt.show()  \n",
    "  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "shape mismatch: objects cannot be broadcast to a single shape.  Mismatch is between arg 0 with shape (5,) and arg 1 with shape (2,).",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32mf:\\Repositories\\Research\\Python\\Python画图\\matplotlib\\test.ipynb 单元格 3\u001b[0m line \u001b[0;36m1\n\u001b[0;32m     <a href='vscode-notebook-cell:/f%3A/Repositories/Research/Python/Python%E7%94%BB%E5%9B%BE/matplotlib/test.ipynb#W6sZmlsZQ%3D%3D?line=11'>12</a>\u001b[0m index \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marange(max_experts)  \n\u001b[0;32m     <a href='vscode-notebook-cell:/f%3A/Repositories/Research/Python/Python%E7%94%BB%E5%9B%BE/matplotlib/test.ipynb#W6sZmlsZQ%3D%3D?line=13'>14</a>\u001b[0m \u001b[39mfor\u001b[39;00m i, (inc, experts) \u001b[39min\u001b[39;00m \u001b[39menumerate\u001b[39m(data\u001b[39m.\u001b[39mitems()):  \n\u001b[1;32m---> <a href='vscode-notebook-cell:/f%3A/Repositories/Research/Python/Python%E7%94%BB%E5%9B%BE/matplotlib/test.ipynb#W6sZmlsZQ%3D%3D?line=14'>15</a>\u001b[0m     plt\u001b[39m.\u001b[39;49mbar(index \u001b[39m+\u001b[39;49m i \u001b[39m*\u001b[39;49m bar_width, \u001b[39mlist\u001b[39;49m(experts\u001b[39m.\u001b[39;49mvalues()), bar_width, label\u001b[39m=\u001b[39;49minc)  \n\u001b[0;32m     <a href='vscode-notebook-cell:/f%3A/Repositories/Research/Python/Python%E7%94%BB%E5%9B%BE/matplotlib/test.ipynb#W6sZmlsZQ%3D%3D?line=16'>17</a>\u001b[0m plt\u001b[39m.\u001b[39mxlabel(\u001b[39m'\u001b[39m\u001b[39mExpert\u001b[39m\u001b[39m'\u001b[39m)  \n\u001b[0;32m     <a href='vscode-notebook-cell:/f%3A/Repositories/Research/Python/Python%E7%94%BB%E5%9B%BE/matplotlib/test.ipynb#W6sZmlsZQ%3D%3D?line=17'>18</a>\u001b[0m plt\u001b[39m.\u001b[39mylabel(\u001b[39m'\u001b[39m\u001b[39mScore\u001b[39m\u001b[39m'\u001b[39m)  \n",
      "File \u001b[1;32mc:\\SoftWare\\Code\\IDE\\Anaconda3\\envs\\torch_hwzhao\\lib\\site-packages\\matplotlib\\pyplot.py:2397\u001b[0m, in \u001b[0;36mbar\u001b[1;34m(x, height, width, bottom, align, data, **kwargs)\u001b[0m\n\u001b[0;32m   2393\u001b[0m \u001b[39m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[39m.\u001b[39mbar)\n\u001b[0;32m   2394\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mbar\u001b[39m(\n\u001b[0;32m   2395\u001b[0m         x, height, width\u001b[39m=\u001b[39m\u001b[39m0.8\u001b[39m, bottom\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, \u001b[39m*\u001b[39m, align\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mcenter\u001b[39m\u001b[39m'\u001b[39m,\n\u001b[0;32m   2396\u001b[0m         data\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m-> 2397\u001b[0m     \u001b[39mreturn\u001b[39;00m gca()\u001b[39m.\u001b[39;49mbar(\n\u001b[0;32m   2398\u001b[0m         x, height, width\u001b[39m=\u001b[39;49mwidth, bottom\u001b[39m=\u001b[39;49mbottom, align\u001b[39m=\u001b[39;49malign,\n\u001b[0;32m   2399\u001b[0m         \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49m({\u001b[39m\"\u001b[39;49m\u001b[39mdata\u001b[39;49m\u001b[39m\"\u001b[39;49m: data} \u001b[39mif\u001b[39;49;00m data \u001b[39mis\u001b[39;49;00m \u001b[39mnot\u001b[39;49;00m \u001b[39mNone\u001b[39;49;00m \u001b[39melse\u001b[39;49;00m {}), \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[1;32mc:\\SoftWare\\Code\\IDE\\Anaconda3\\envs\\torch_hwzhao\\lib\\site-packages\\matplotlib\\__init__.py:1414\u001b[0m, in \u001b[0;36m_preprocess_data.<locals>.inner\u001b[1;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1411\u001b[0m \u001b[39m@functools\u001b[39m\u001b[39m.\u001b[39mwraps(func)\n\u001b[0;32m   1412\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39minner\u001b[39m(ax, \u001b[39m*\u001b[39margs, data\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[0;32m   1413\u001b[0m     \u001b[39mif\u001b[39;00m data \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m-> 1414\u001b[0m         \u001b[39mreturn\u001b[39;00m func(ax, \u001b[39m*\u001b[39;49m\u001b[39mmap\u001b[39;49m(sanitize_sequence, args), \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[0;32m   1416\u001b[0m     bound \u001b[39m=\u001b[39m new_sig\u001b[39m.\u001b[39mbind(ax, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m   1417\u001b[0m     auto_label \u001b[39m=\u001b[39m (bound\u001b[39m.\u001b[39marguments\u001b[39m.\u001b[39mget(label_namer)\n\u001b[0;32m   1418\u001b[0m                   \u001b[39mor\u001b[39;00m bound\u001b[39m.\u001b[39mkwargs\u001b[39m.\u001b[39mget(label_namer))\n",
      "File \u001b[1;32mc:\\SoftWare\\Code\\IDE\\Anaconda3\\envs\\torch_hwzhao\\lib\\site-packages\\matplotlib\\axes\\_axes.py:2345\u001b[0m, in \u001b[0;36mAxes.bar\u001b[1;34m(self, x, height, width, bottom, align, **kwargs)\u001b[0m\n\u001b[0;32m   2342\u001b[0m     \u001b[39mif\u001b[39;00m yerr \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m   2343\u001b[0m         yerr \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_convert_dx(yerr, y0, y, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconvert_yunits)\n\u001b[1;32m-> 2345\u001b[0m x, height, width, y, linewidth, hatch \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39;49mbroadcast_arrays(\n\u001b[0;32m   2346\u001b[0m     \u001b[39m# Make args iterable too.\u001b[39;49;00m\n\u001b[0;32m   2347\u001b[0m     np\u001b[39m.\u001b[39;49matleast_1d(x), height, width, y, linewidth, hatch)\n\u001b[0;32m   2349\u001b[0m \u001b[39m# Now that units have been converted, set the tick locations.\u001b[39;00m\n\u001b[0;32m   2350\u001b[0m \u001b[39mif\u001b[39;00m orientation \u001b[39m==\u001b[39m \u001b[39m'\u001b[39m\u001b[39mvertical\u001b[39m\u001b[39m'\u001b[39m:\n",
      "File \u001b[1;32m<__array_function__ internals>:180\u001b[0m, in \u001b[0;36mbroadcast_arrays\u001b[1;34m(*args, **kwargs)\u001b[0m\n",
      "File \u001b[1;32mc:\\SoftWare\\Code\\IDE\\Anaconda3\\envs\\torch_hwzhao\\lib\\site-packages\\numpy\\lib\\stride_tricks.py:540\u001b[0m, in \u001b[0;36mbroadcast_arrays\u001b[1;34m(subok, *args)\u001b[0m\n\u001b[0;32m    533\u001b[0m \u001b[39m# nditer is not used here to avoid the limit of 32 arrays.\u001b[39;00m\n\u001b[0;32m    534\u001b[0m \u001b[39m# Otherwise, something like the following one-liner would suffice:\u001b[39;00m\n\u001b[0;32m    535\u001b[0m \u001b[39m# return np.nditer(args, flags=['multi_index', 'zerosize_ok'],\u001b[39;00m\n\u001b[0;32m    536\u001b[0m \u001b[39m#                  order='C').itviews\u001b[39;00m\n\u001b[0;32m    538\u001b[0m args \u001b[39m=\u001b[39m [np\u001b[39m.\u001b[39marray(_m, copy\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m, subok\u001b[39m=\u001b[39msubok) \u001b[39mfor\u001b[39;00m _m \u001b[39min\u001b[39;00m args]\n\u001b[1;32m--> 540\u001b[0m shape \u001b[39m=\u001b[39m _broadcast_shape(\u001b[39m*\u001b[39;49margs)\n\u001b[0;32m    542\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mall\u001b[39m(array\u001b[39m.\u001b[39mshape \u001b[39m==\u001b[39m shape \u001b[39mfor\u001b[39;00m array \u001b[39min\u001b[39;00m args):\n\u001b[0;32m    543\u001b[0m     \u001b[39m# Common case where nothing needs to be broadcasted.\u001b[39;00m\n\u001b[0;32m    544\u001b[0m     \u001b[39mreturn\u001b[39;00m args\n",
      "File \u001b[1;32mc:\\SoftWare\\Code\\IDE\\Anaconda3\\envs\\torch_hwzhao\\lib\\site-packages\\numpy\\lib\\stride_tricks.py:422\u001b[0m, in \u001b[0;36m_broadcast_shape\u001b[1;34m(*args)\u001b[0m\n\u001b[0;32m    417\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Returns the shape of the arrays that would result from broadcasting the\u001b[39;00m\n\u001b[0;32m    418\u001b[0m \u001b[39msupplied arrays against each other.\u001b[39;00m\n\u001b[0;32m    419\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m    420\u001b[0m \u001b[39m# use the old-iterator because np.nditer does not handle size 0 arrays\u001b[39;00m\n\u001b[0;32m    421\u001b[0m \u001b[39m# consistently\u001b[39;00m\n\u001b[1;32m--> 422\u001b[0m b \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39;49mbroadcast(\u001b[39m*\u001b[39;49margs[:\u001b[39m32\u001b[39;49m])\n\u001b[0;32m    423\u001b[0m \u001b[39m# unfortunately, it cannot handle 32 or more arguments directly\u001b[39;00m\n\u001b[0;32m    424\u001b[0m \u001b[39mfor\u001b[39;00m pos \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39m32\u001b[39m, \u001b[39mlen\u001b[39m(args), \u001b[39m31\u001b[39m):\n\u001b[0;32m    425\u001b[0m     \u001b[39m# ironically, np.broadcast does not properly handle np.broadcast\u001b[39;00m\n\u001b[0;32m    426\u001b[0m     \u001b[39m# objects (it treats them as scalars)\u001b[39;00m\n\u001b[0;32m    427\u001b[0m     \u001b[39m# use broadcasting to avoid allocating the full array\u001b[39;00m\n",
      "\u001b[1;31mValueError\u001b[0m: shape mismatch: objects cannot be broadcast to a single shape.  Mismatch is between arg 0 with shape (5,) and arg 1 with shape (2,)."
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 但由于上面的代码会导致柱状图重叠，我们可以使用另一种方法来绘制分组柱状图：  \n",
    "# 给定的数据  \n",
    "data = {  \n",
    "    'Inc 0': {'Expert 0': 2000},  \n",
    "    'Inc 1': {'Expert 0': 2174, 'Expert 1': 1826},  \n",
    "    'Inc 2': {'Expert 0': 1804, 'Expert 1': 2389, 'Expert 2': 1807},  \n",
    "    'Inc 3': {'Expert 0': 2013, 'Expert 1': 2977, 'Expert 2': 2400, 'Expert 3': 610},  \n",
    "    'Inc 4': {'Expert 0': 1483, 'Expert 1': 3631, 'Expert 2': 2091, 'Expert 3': 1474, 'Expert 4': 1321}  \n",
    "} \n",
    "plt.figure(figsize=(10, 6))  \n",
    "bar_width = 0.25  \n",
    "index = np.arange(max_experts)  \n",
    "  \n",
    "for i, (inc, experts) in enumerate(data.items()):  \n",
    "    plt.bar(index + i * bar_width, list(experts.values()), bar_width, label=inc)  \n",
    "  \n",
    "plt.xlabel('Expert')  \n",
    "plt.ylabel('Score')  \n",
    "plt.xticks(index + bar_width / 2, ['Expert {}'.format(i) for i in range(max_experts)])  \n",
    "plt.legend()  \n",
    "plt.title('Scores by Inc and Expert (Grouped)')  \n",
    "plt.tight_layout()  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA90AAAJOCAYAAACqS2TfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAABSUUlEQVR4nO3de1iUdeL//9eIHJVDqDCYqCR4IMXUTMnWSEVQMy36bJqbWKbZYqaUGLumWJbmrpqVZm152M3K7OAa5gExrRSxTMo8lUaLrgxUBiOagDC/P/o532ZRY4zb4fB8XNdcl/d9v+d9v+6p66qX98lks9lsAgAAAAAANa6RqwMAAAAAAFBfUboBAAAAADAIpRsAAAAAAINQugEAAAAAMAilGwAAAAAAg1C6AQAAAAAwCKUbAAAAAACDULoBAAAAADBIY1cHqAsqKyt14sQJ+fr6ymQyuToOAAAAAMDFbDabTp06pZYtW6pRo4ufz6Z0V8OJEycUGhrq6hgAAAAAgFrm2LFjatWq1UW3U7qrwdfXV9IvP6afn5+L0wAAAAAAXM1qtSo0NNTeFy+G0l0N5y8p9/Pzo3QDAAAAAOx+6xZkHqQGAAAAAIBBKN0AAAAAABiE0g0AAAAAgEG4p7sGVVRUqLy83NUx6ix3d3e5ubm5OgYAAAAA1BhKdw2w2WyyWCwqKipydZQ6LyAgQGazmfehAwAAAKgXKN014HzhDgoKko+PD4XxMthsNp05c0aFhYWSpJCQEBcnAgAAAIDfj9L9O1VUVNgLd7NmzVwdp07z9vaWJBUWFiooKIhLzQEAAADUeTxI7Xc6fw+3j4+Pi5PUD+d/R+6NBwAAAFAfULprCJeU1wx+RwAAAAD1CaUbAAAAAACDULoBAAAAADAID1IzUNvH1l/R/X03d4hT48eMGaOioiKtXbvWmECSzp49q0ceeURvvvmmSktLFRcXpyVLlig4ONiwfQIAAABAbcGZbhhqypQpev/997VmzRpt375dJ06c0B133OHqWAAAAABwRVC6YRcTE6NJkyYpJSVFgYGBMpvNSktLcxhTVFSkBx54QMHBwfLy8lLnzp2Vnp5+wfmKi4v16quvasGCBerXr5969Oih5cuXa+fOndq1a9cVOCIAAAAAcC0uL4eDlStXKjk5WdnZ2crKytKYMWPUp08fxcbGqrKyUoMGDdKpU6f02muvqV27djpw4MBF36e9Z88elZeXa8CAAfZ1HTt2VOvWrZWVlaXevXtfqcMCAAAAAJegdMNBVFSUZs6cKUmKiIjQCy+8oMzMTMXGxmrLli3avXu3Dh48qPbt20uSrrnmmovOZbFY5OHhoYCAAIf1wcHBslgshh0DAAAAANQWXF4OB1FRUQ7LISEhKiwslCTl5OSoVatW9sINAAAAALg0SjccuLu7OyybTCZVVlZKkry9vZ2ay2w2q6ysTEVFRQ7rCwoKZDabf1dOAAAAAKgLKN2otqioKB0/flxff/11tcb36NFD7u7uyszMtK87fPiw8vLyFB0dbVRMAAAAAKg1uKcb1XbzzTerb9++SkhI0IIFCxQeHq5Dhw7JZDIpPj6+ynh/f3+NHTtWycnJCgwMlJ+fnx566CFFR0fzEDUAAAAADQJnuuGUd955Rz179tTIkSMVGRmplJQUVVRUXHT8woULdeuttyohIUF9+/aV2WzWu+++ewUTAwAAAIDrmGw2m83VIWo7q9Uqf39/FRcXy8/Pz2Hb2bNnlZubq7CwMHl5ebkoYf3B7wkAAACgLrhUT/w1Li8HAAAAoMUTtro6AuAgaWk/V0eoEVxeDgAAAACAQSjdAAAAAAAYhNINAAAAAIBBKN0AAAAAABiE0g0AAAAAgEEo3QAAAAAAGITSDQAAAACAQSjdAAAAAAAYhNLdgI0ZM0bDhw83dB8vv/yyYmJi5OfnJ5PJpKKiIkP3BwAAAAC1SWNXB6jX0vyv8P6Kr+z+quHMmTOKj49XfHy8UlNTXR0HAAAAAK4oznTDLiYmRpMmTVJKSooCAwNlNpuVlpbmMKaoqEgPPPCAgoOD5eXlpc6dOys9Pf2ic06ePFmPPfaYevfubXB6AAAAAKh9ONMNBytXrlRycrKys7OVlZWlMWPGqE+fPoqNjVVlZaUGDRqkU6dO6bXXXlO7du104MABubm5uTo2AAAAANRKlG44iIqK0syZMyVJEREReuGFF5SZmanY2Fht2bJFu3fv1sGDB9W+fXtJ0jXXXOPKuAAAAABQq7n08vIXX3xRUVFR8vPzk5+fn6Kjo7Vhwwb79piYGJlMJofPhAkTHObIy8vTkCFD5OPjo6CgIE2dOlXnzp1zGLNt2zZ1795dnp6eCg8P14oVK67E4dVJUVFRDsshISEqLCyUJOXk5KhVq1b2wg0AAAAAuDSXnulu1aqV5s6dq4iICNlsNq1cuVLDhg3T3r17de2110qSxo0bpyeeeML+HR8fH/ufKyoqNGTIEJnNZu3cuVP5+fkaPXq03N3d9fTTT0uScnNzNWTIEE2YMEGrVq1SZmam7r//foWEhCguLu7KHnAd4O7u7rBsMplUWVkpSfL29nZFJAAAAACos1xauocOHeqw/NRTT+nFF1/Url277KXbx8dHZrP5gt/fvHmzDhw4oC1btig4OFjXXXednnzySU2bNk1paWny8PDQ0qVLFRYWpvnz50uSOnXqpE8++UQLFy6kdDspKipKx48f19dff83ZbgAAAACohlrz9PKKigq9+eabOn36tKKjo+3rV61apebNm6tz585KTU3VmTNn7NuysrLUpUsXBQcH29fFxcXJarVq//799jEDBgxw2FdcXJyysrIumqW0tFRWq9XhA+nmm29W3759lZCQoIyMDOXm5mrDhg3auHHjRb9jsViUk5OjI0eOSJL27dunnJwcnTx58krFBgAAAACXcXnp3rdvn5o2bSpPT09NmDBB7733niIjIyVJd999t1577TV9+OGHSk1N1b/+9S/96U9/sn/XYrE4FG5J9mWLxXLJMVarVT///PMFM82ZM0f+/v72T2hoaI0db133zjvvqGfPnho5cqQiIyOVkpKiioqKi45funSpunXrpnHjxkmS+vbtq27dumndunVXKjIAAAAAuIzLn17eoUMH5eTkqLi4WG+//bYSExO1fft2RUZGavz48fZxXbp0UUhIiPr376+jR4+qXbt2hmVKTU1VcnKyfdlqtV5e8U4rrsFUNe9/Hyi3bdu2KmPWrl3rsBwYGKhly5ZVex9paWlV3vUNAAAAAA2Fy890e3h4KDw8XD169NCcOXPUtWtXLVq06IJje/XqJUn2S5XNZrMKCgocxpxfPn8f+MXG+Pn5XfTBYJ6envYnqp//AAAAAADgLJeX7v9VWVmp0tLSC27LycmR9MtrrCQpOjpa+/bts7/SSpIyMjLk5+dnv0Q9OjpamZmZDvNkZGQ43DcOAAAAAIARXHp5eWpqqgYNGqTWrVvr1KlTev3117Vt2zZt2rRJR48e1euvv67BgwerWbNm+vLLLzVlyhT17dvX/i7pgQMHKjIyUvfcc4/mzZsni8Wi6dOnKykpSZ6enpKkCRMm6IUXXlBKSoruu+8+bd26VW+99ZbWr1/vykMHAAAAADQALi3dhYWFGj16tPLz8+Xv76+oqCht2rRJsbGxOnbsmLZs2aJnn31Wp0+fVmhoqBISEjR9+nT7993c3JSenq4HH3xQ0dHRatKkiRITEx3e6x0WFqb169drypQpWrRokVq1aqVXXnmF14UBAAAAAAzn0tL96quvXnRbaGiotm/f/ptztGnTRh988MElx8TExGjv3r1O5wMAAAAA4Peodfd0AwAAAABQX1C6AQAAAAAwCKUbAAAAAACDULoBAAAAADAIpRsAAAAAAINQuhuwMWPGaPjw4YbNf/LkST300EPq0KGDvL291bp1a02aNEnFxcWG7RMAAAAAahOXvjKsvuuysssV3d++xH1XdH+/5cSJEzpx4oT+/ve/KzIyUv/5z380YcIEnThxQm+//bar4wEAAACA4TjTDbuYmBhNmjRJKSkpCgwMlNlsVlpamsOYoqIiPfDAAwoODpaXl5c6d+6s9PT0C87XuXNnvfPOOxo6dKjatWunfv366amnntL777+vc+fOXYEjAgAAAADX4kw3HKxcuVLJycnKzs5WVlaWxowZoz59+ig2NlaVlZUaNGiQTp06pddee03t2rXTgQMH5ObmVu35i4uL5efnp8aN+VcPAAAAQP1H84GDqKgozZw5U5IUERGhF154QZmZmYqNjdWWLVu0e/duHTx4UO3bt5ckXXPNNdWe+4cfftCTTz6p8ePHG5IdAAAAAGobLi+Hg6ioKIflkJAQFRYWSpJycnLUqlUre+F2htVq1ZAhQxQZGVnlknUAAAAAqK8o3XDg7u7usGwymVRZWSlJ8vb2vqw5T506pfj4ePn6+uq9996rsg8AAAAAqK8o3ai2qKgoHT9+XF9//XW1v2O1WjVw4EB5eHho3bp18vLyMjAhAAAAANQu3NONarv55pvVt29fJSQkaMGCBQoPD9ehQ4dkMpkUHx9fZfz5wn3mzBm99tprslqtslqtkqQWLVo49QA2AAAAAKiLKN1wyjvvvKNHH31UI0eO1OnTpxUeHq65c+decOznn3+u7OxsSVJ4eLjDttzcXLVt29bouAAAAADgUiabzWZzdYjazmq1yt/f3/66q187e/ascnNzFRYWxqXTNYDfEwAAwDUWT9jq6giAg6Sl/Vwd4ZIu1RN/jXu6AQAAAAAwCKUbAAAAAACDULoBAAAAADAIpRsAAAAAAINQugEAAAAAMAilGwAAAAAAg1C6AQAAAAAwCKUbAAAAAACDULoBAAAAADAIpRsAAAAAAINQuhuwMWPGaPjw4Ybu44EHHlC7du3k7e2tFi1aaNiwYTp06JCh+wQAAACA2qKxqwPUZwc7drqi++t06OAV3V919OjRQ6NGjVLr1q118uRJpaWlaeDAgcrNzZWbm5ur4wEAAACAoTjTDbuYmBhNmjRJKSkpCgwMlNlsVlpamsOYoqIiPfDAAwoODpaXl5c6d+6s9PT0i845fvx49e3bV23btlX37t01e/ZsHTt2TN99952xBwMAAAAAtQBnuuFg5cqVSk5OVnZ2trKysjRmzBj16dNHsbGxqqys1KBBg3Tq1Cm99tprateunQ4cOFDtM9anT5/W8uXLFRYWptDQUIOPBAAAAABcj9INB1FRUZo5c6YkKSIiQi+88IIyMzMVGxurLVu2aPfu3Tp48KDat28vSbrmmmt+c84lS5YoJSVFp0+fVocOHZSRkSEPDw9DjwMAAAAAagMuL4eDqKgoh+WQkBAVFhZKknJyctSqVSt74a6uUaNGae/evdq+fbvat2+vP/7xjzp79myNZQYAAACA2ooz3XDg7u7usGwymVRZWSlJ8vb2vqw5/f395e/vr4iICPXu3VtXXXWV3nvvPY0cOfJ35wUAAACA2owz3ai2qKgoHT9+XF9//fVlz2Gz2WSz2VRaWlqDyQAAAACgduJMN6rt5ptvVt++fZWQkKAFCxYoPDxchw4dkslkUnx8fJXx3377rVavXq2BAweqRYsWOn78uObOnStvb28NHjzYBUcAAAAAAFcWZ7rhlHfeeUc9e/bUyJEjFRkZqZSUFFVUVFxwrJeXlz7++GMNHjxY4eHhuuuuu+Tr66udO3cqKCjoCicHAAAAgCvPZLPZbK4OUdtZrVb5+/uruLhYfn5+DtvOnj2r3NxchYWFycvLy0UJ6w9+TwAAANdYPGGrqyMADpKW9nN1hEu6VE/8Nc50AwAAAABgEEo3AAAAAAAGoXQDAAAAAGAQSjcAAAAAAAahdAMAAAAAYBBKNwAAAAAABqF0AwAAAABgEEo3AAAAAAAGoXQDAAAAAGAQSjcAAAAAAAahdDdgY8aM0fDhw6/Ivmw2mwYNGiSTyaS1a9dekX0CAAAAgKs1dnWA+mzxhK1XdH9JS/td0f0549lnn5XJZHJ1DAAAAAC4ojjTDbuYmBhNmjRJKSkpCgwMlNlsVlpamsOYoqIiPfDAAwoODpaXl5c6d+6s9PT0S86bk5Oj+fPna9myZQamBwAAAIDahzPdcLBy5UolJycrOztbWVlZGjNmjPr06aPY2FhVVlZq0KBBOnXqlF577TW1a9dOBw4ckJub20XnO3PmjO6++24tXrxYZrP5Ch4JAAAAALgepRsOoqKiNHPmTElSRESEXnjhBWVmZio2NlZbtmzR7t27dfDgQbVv316SdM0111xyvilTpujGG2/UsGHDDM8OAAAAALUNpRsOoqKiHJZDQkJUWFgo6ZfLxFu1amUv3L9l3bp12rp1q/bu3VvjOQEAAACgLuCebjhwd3d3WDaZTKqsrJQkeXt7OzXX1q1bdfToUQUEBKhx48Zq3PiXv+NJSEhQTExMjeQFAAAAgNqMM92otqioKB0/flxff/11tc52P/bYY7r//vsd1nXp0kULFy7U0KFDjYoJAAAAALUGpRvVdvPNN6tv375KSEjQggULFB4erkOHDslkMik+Pr7KeLPZfMGHp7Vu3VphYWFXIjIAAAAAuBSXl8Mp77zzjnr27KmRI0cqMjJSKSkpqqiocHUsAAAAAKiVONNtoKSl/Vwd4ZJWrFjhsLxt27YqY9auXeuwHBgY+Lvet22z2S77uwAAAABQ17j0TPeLL76oqKgo+fn5yc/PT9HR0dqwYYN9+9mzZ5WUlKRmzZqpadOmSkhIUEFBgcMceXl5GjJkiHx8fBQUFKSpU6fq3LlzDmO2bdum7t27y9PTU+Hh4VXKJgAAAAAARnBp6W7VqpXmzp2rPXv26LPPPlO/fv00bNgw7d+/X9Iv73h+//33tWbNGm3fvl0nTpzQHXfcYf9+RUWFhgwZorKyMu3cuVMrV67UihUrNGPGDPuY3NxcDRkyRLfccotycnI0efJk3X///dq0adMVP14AAAAAQMNistWy630DAwP1t7/9TXfeeadatGih119/XXfeeack6dChQ+rUqZOysrLUu3dvbdiwQbfeeqtOnDih4OBgSdLSpUs1bdo0ff/99/Lw8NC0adO0fv16ffXVV/Z9jBgxQkVFRdq4cWO1MlmtVvn7+6u4uFh+fn4O286ePavc3FyFhYXJy8urhn6FhovfEwAAwDUWT9jq6giAg9p+u+6leuKv1ZoHqVVUVOjNN9/U6dOnFR0drT179qi8vFwDBgywj+nYsaNat26trKwsSVJWVpa6dOliL9ySFBcXJ6vVaj9bnpWV5TDH+THn57iQ0tJSWa1Whw8AAAAAAM5yeenet2+fmjZtKk9PT02YMEHvvfeeIiMjZbFY5OHhoYCAAIfxwcHBslgskiSLxeJQuM9vP7/tUmOsVqt+/vnnC2aaM2eO/P397Z/Q0NCaOFQAAAAAQAPj8tLdoUMH5eTkKDs7Ww8++KASExN14MABl2ZKTU1VcXGx/XPs2LHf/E4tu0q/zuJ3BAAAAFCfuPyVYR4eHgoPD5ck9ejRQ59++qkWLVqku+66S2VlZSoqKnI4211QUCCz2SxJMpvN2r17t8N8559u/usx//vE84KCAvn5+cnb2/uCmTw9PeXp6Vmt/O7u7pKkM2fOXHQ+VN+ZM2ck/b/fFQAAAADqMpeX7v9VWVmp0tJS9ejRQ+7u7srMzFRCQoIk6fDhw8rLy1N0dLQkKTo6Wk899ZQKCwsVFBQkScrIyJCfn58iIyPtYz744AOHfWRkZNjn+L3c3NwUEBCgwsJCSZKPj49MJlONzN2Q2Gw2nTlzRoWFhQoICJCbm5urIwEAYLiDHTu5OgLw/8QsdnUCoF5yaelOTU3VoEGD1Lp1a506dUqvv/66tm3bpk2bNsnf319jx45VcnKyAgMD5efnp4ceekjR0dHq3bu3JGngwIGKjIzUPffco3nz5slisWj69OlKSkqyn6meMGGCXnjhBaWkpOi+++7T1q1b9dZbb2n9+vU1dhznz6qfL964fAEBAfbfEwAAAADqOpeW7sLCQo0ePVr5+fny9/dXVFSUNm3apNjYWEnSwoUL1ahRIyUkJKi0tFRxcXFasmSJ/ftubm5KT0/Xgw8+qOjoaDVp0kSJiYl64okn7GPCwsK0fv16TZkyRYsWLVKrVq30yiuvKC4ursaOw2QyKSQkREFBQSovL6+xeRsad3d3znADAAAAqFdq3Xu6a6Pqvn8NAADAGVxejtpkK5eXo5bhPd0AAAAAAOCSKN0AAAAAABiE0g0AAAAAgEEo3QAAAAAAGITSDQAAAACAQSjdAAAAAAAYhNINAAAAAIBBKN0AAAAAABiE0g0AAAAAgEEo3QAAAAAAGITSDQAAAACAQSjdAAAAAAAYhNINAAAAAIBBKN0AAAAAABiE0g0AAAAAgEEo3QAAAAAAGITSDQAAAACAQSjdAAAAAAAYhNINAAAAAIBBKN0AAAAAABiE0g0AAAAAgEEo3QAAAAAAGITSDQAAAACAQSjdAAAAAAAYhNINAAAAAIBBKN0AAAAAABiE0g0AAAAAgEEo3QAAAAAAGITSDQAAAACAQSjdAAAAAAAYhNINAAAAAIBBKN0AAAAAABiE0g0AAAAAgEEo3QAAAAAAGITSDQAAAACAQSjdAAAAAAAYhNINAAAAAIBBKN0AAAAAABiE0g0AAAAAgEEo3QAAAAAAGITSDQAAAACAQSjdAAAAAAAYhNINAAAAAIBBKN0AAAAAABiE0g0AAAAAgEEo3QAAAAAAGITSDQAAAACAQSjdAAAAAAAYhNINAAAAAIBBKN0AAAAAABiE0g0AAAAAgEEo3QAAAAAAGITSDQAAAACAQSjdAAAAAAAYhNINAAAAAIBBKN0AAAAAABiE0g0AAAAAgEEo3QAAAAAAGMSlpXvOnDnq2bOnfH19FRQUpOHDh+vw4cMOY2JiYmQymRw+EyZMcBiTl5enIUOGyMfHR0FBQZo6darOnTvnMGbbtm3q3r27PD09FR4erhUrVhh9eAAAAACABs6lpXv79u1KSkrSrl27lJGRofLycg0cOFCnT592GDdu3Djl5+fbP/PmzbNvq6io0JAhQ1RWVqadO3dq5cqVWrFihWbMmGEfk5ubqyFDhuiWW25RTk6OJk+erPvvv1+bNm26YscKAAAAAGh4Grty5xs3bnRYXrFihYKCgrRnzx717dvXvt7Hx0dms/mCc2zevFkHDhzQli1bFBwcrOuuu05PPvmkpk2bprS0NHl4eGjp0qUKCwvT/PnzJUmdOnXSJ598ooULFyouLs64AwQAAAAANGi16p7u4uJiSVJgYKDD+lWrVql58+bq3LmzUlNTdebMGfu2rKwsdenSRcHBwfZ1cXFxslqt2r9/v33MgAEDHOaMi4tTVlbWBXOUlpbKarU6fAAAAAAAcJZLz3T/WmVlpSZPnqw+ffqoc+fO9vV333232rRpo5YtW+rLL7/UtGnTdPjwYb377ruSJIvF4lC4JdmXLRbLJcdYrVb9/PPP8vb2dtg2Z84czZo1q8aPEQAAAADQsNSa0p2UlKSvvvpKn3zyicP68ePH2//cpUsXhYSEqH///jp69KjatWtnSJbU1FQlJyfbl61Wq0JDQw3ZFwAAAACg/qoVl5dPnDhR6enp+vDDD9WqVatLju3Vq5ck6ciRI5Iks9msgoIChzHnl8/fB36xMX5+flXOckuSp6en/Pz8HD4AAAAAADjLpaXbZrNp4sSJeu+997R161aFhYX95ndycnIkSSEhIZKk6Oho7du3T4WFhfYxGRkZ8vPzU2RkpH1MZmamwzwZGRmKjo6uoSMBAAAAAKAql5bupKQkvfbaa3r99dfl6+sri8Uii8Win3/+WZJ09OhRPfnkk9qzZ4++++47rVu3TqNHj1bfvn0VFRUlSRo4cKAiIyN1zz336IsvvtCmTZs0ffp0JSUlydPTU5I0YcIEffvtt0pJSdGhQ4e0ZMkSvfXWW5oyZYrLjh0AAAAAUP+5tHS/+OKLKi4uVkxMjEJCQuyf1atXS5I8PDy0ZcsWDRw4UB07dtQjjzyihIQEvf/++/Y53NzclJ6eLjc3N0VHR+tPf/qTRo8erSeeeMI+JiwsTOvXr1dGRoa6du2q+fPn65VXXuF1YQAAAAAAQ7n0QWo2m+2S20NDQ7V9+/bfnKdNmzb64IMPLjkmJiZGe/fudSofAAAAAAC/R614kBoAAAAAAPURpRsAAAAAAINQugEAAAAAMAilGwAAAAAAg1C6AQAAAAAwCKUbAAAAAACDULoBAAAAADAIpRsAAAAAAINQugEAAAAAMEhjVwcAAOBK6bKyi6sjAA7ecnUAAIDhONMNAAAAAIBBLqt0Hz16VNOnT9fIkSNVWFgoSdqwYYP2799fo+EAAAAAAKjLnC7d27dvV5cuXZSdna13331XJSUlkqQvvvhCM2fOrPGAAAAAAADUVU6X7scee0yzZ89WRkaGPDw87Ov79eunXbt21Wg4AAAAAADqMqdL9759+3T77bdXWR8UFKQffvihRkIBAAAAAFAfOF26AwIClJ+fX2X93r17dfXVV9dIKAAAAAAA6gOnS/eIESM0bdo0WSwWmUwmVVZWaseOHXr00Uc1evRoIzICAAAAAFAnOV26n376aXXs2FGhoaEqKSlRZGSk+vbtqxtvvFHTp083IiMAAAAAAHVSY2cG22w2WSwWPffcc5oxY4b27dunkpISdevWTREREUZlBAAAAACgTnK6dIeHh2v//v2KiIhQaGioUbkAAAAAAKjznLq8vFGjRoqIiNCPP/5oVB4AAAAAAOoNp+/pnjt3rqZOnaqvvvrKiDwAAAAAANQbTl1eLkmjR4/WmTNn1LVrV3l4eMjb29th+8mTJ2ssHAAAAAAAdZnTpfvZZ581IAYAAAAAAPWP06U7MTHRiBwAAAAAANQ7TpduSaqoqNDatWt18OBBSdK1116r2267TW5ubjUaDgAAAACAuszp0n3kyBENHjxY//3vf9WhQwdJ0pw5cxQaGqr169erXbt2NR4SAAAAAIC6yOmnl0+aNEnt2rXTsWPH9Pnnn+vzzz9XXl6ewsLCNGnSJCMyAgAAAABQJzl9pnv79u3atWuXAgMD7euaNWumuXPnqk+fPjUaDgAAAACAuszpM92enp46depUlfUlJSXy8PCokVAAAAAAANQHTpfuW2+9VePHj1d2drZsNptsNpt27dqlCRMm6LbbbjMiIwAAAAAAdZLTpfu5555Tu3btFB0dLS8vL3l5ealPnz4KDw/XokWLjMgIAAAAAECd5PQ93QEBAfr3v/+tI0eO2F8Z1qlTJ4WHh9d4OAAAAAAA6rLLek+3JIWHh1O0AQAAAAC4BKcvL09ISNAzzzxTZf28efP0f//3fzUSCgAAAACA+sDp0v3RRx9p8ODBVdYPGjRIH330UY2EAgAAAACgPnC6dF/s1WDu7u6yWq01EgoAAAAAgPrA6dLdpUsXrV69usr6N998U5GRkTUSCgAAAACA+sDpB6k9/vjjuuOOO3T06FH169dPkpSZmak33nhDa9asqfGAAOqwNH9XJwAchbV2dQIAANDAOF26hw4dqrVr1+rpp5/W22+/LW9vb0VFRWnLli26+eabjcgIAAAAAECddFmvDBsyZIiGDBlS01kAAAAAAKhXLvs93ZJ09uxZrV69WqdPn1ZsbKwiIiJqKhcAAAAAAHVetUt3cnKyysvL9fzzz0uSysrK1Lt3bx04cEA+Pj5KSUlRRkaGoqOjDQsLAAAAAEBdUu2nl2/evFmxsbH25VWrVikvL0/ffPONfvrpJ/3f//2fZs+ebUhIAAAAAADqomqX7ry8PIdXgm3evFl33nmn2rRpI5PJpIcfflh79+41JCQAAAAAAHVRtUt3o0aNZLPZ7Mu7du1S79697csBAQH66aefajYdAAAAAAB1WLVLd6dOnfT+++9Lkvbv36+8vDzdcsst9u3/+c9/FBwcXPMJAQAAAACoo6r9ILWUlBSNGDFC69ev1/79+zV48GCFhYXZt3/wwQe64YYbDAkJAAAAAEBdVO0z3bfffrs++OADRUVFacqUKVq9erXDdh8fH/35z3+u8YAAAAAAANRVTr2nu3///urfv/8Ft82cObNGAgEAAAAAUF9U+0w3AAAAAABwDqUbAAAAAACDULoBAAAAADAIpRsAAAAAAINcVuk+d+6ctmzZopdeekmnTp2SJJ04cUIlJSU1Gg4AAAAAgLrMqaeXS9J//vMfxcfHKy8vT6WlpYqNjZWvr6+eeeYZlZaWaunSpUbkBAAAAACgznH6TPfDDz+s66+/Xj/99JO8vb3t62+//XZlZmbWaDgAAAAAAOoyp890f/zxx9q5c6c8PDwc1rdt21b//e9/aywYAAAAAAB1ndNnuisrK1VRUVFl/fHjx+Xr6+vUXHPmzFHPnj3l6+uroKAgDR8+XIcPH3YYc/bsWSUlJalZs2Zq2rSpEhISVFBQ4DAmLy9PQ4YMkY+Pj4KCgjR16lSdO3fOYcy2bdvUvXt3eXp6Kjw8XCtWrHAqKwAAAAAAznK6dA8cOFDPPvusfdlkMqmkpEQzZ87U4MGDnZpr+/btSkpK0q5du5SRkaHy8nINHDhQp0+fto+ZMmWK3n//fa1Zs0bbt2/XiRMndMcdd9i3V1RUaMiQISorK9POnTu1cuVKrVixQjNmzLCPyc3N1ZAhQ3TLLbcoJydHkydP1v33369NmzY5e/gAAAAAAFSbyWaz2Zz5wvHjxxUXFyebzaZvvvlG119/vb755hs1b95cH330kYKCgi47zPfff6+goCBt375dffv2VXFxsVq0aKHXX39dd955pyTp0KFD6tSpk7KystS7d29t2LBBt956q06cOKHg4GBJ0tKlSzVt2jR9//338vDw0LRp07R+/Xp99dVX9n2NGDFCRUVF2rhx42/mslqt8vf3V3Fxsfz8/C77+IAGJ83f1QkAB13CWrs6AuDgrTnnfnsQcIVsjVns6giAg6Sl/Vwd4ZKq2xOdPtPdqlUrffHFF/rrX/+qKVOmqFu3bpo7d6727t37uwq3JBUXF0uSAgMDJUl79uxReXm5BgwYYB/TsWNHtW7dWllZWZKkrKwsdenSxV64JSkuLk5Wq1X79++3j/n1HOfHnJ/jf5WWlspqtTp8AAAAAABwltMPUpOkxo0ba9SoURo1alSNBamsrNTkyZPVp08fde7cWZJksVjk4eGhgIAAh7HBwcGyWCz2Mb8u3Oe3n992qTFWq1U///yzw1PYpV/uNZ81a1aNHduV1Pax9a6OANh95+XqBAAAAIBrOX2me86cOVq2bFmV9cuWLdMzzzxz2UGSkpL01Vdf6c0337zsOWpKamqqiouL7Z9jx465OhIAAAAAoA5yunS/9NJL6tixY5X11157rZYuXXpZISZOnKj09HR9+OGHatWqlX292WxWWVmZioqKHMYXFBTIbDbbx/zv08zPL//WGD8/vypnuSXJ09NTfn5+Dh8AAAAAAJzldOm2WCwKCQmpsr5FixbKz893ai6bzaaJEyfqvffe09atWxUWFuawvUePHnJ3d1dmZqZ93eHDh5WXl6fo6GhJUnR0tPbt26fCwkL7mIyMDPn5+SkyMtI+5tdznB9zfg4AAAAAAIzgdOkODQ3Vjh07qqzfsWOHWrZs6dRcSUlJeu211/T666/L19dXFotFFotFP//8syTJ399fY8eOVXJysj788EPt2bNH9957r6Kjo9W7d29Jv7zCLDIyUvfcc4+++OILbdq0SdOnT1dSUpI8PT0lSRMmTNC3336rlJQUHTp0SEuWLNFbb72lKVOmOHv4AAAAAABUm9MPUhs3bpwmT56s8vJy9ev3yyPcMzMzlZKSokceecSpuV588UVJUkxMjMP65cuXa8yYMZKkhQsXqlGjRkpISFBpaani4uK0ZMkS+1g3Nzelp6frwQcfVHR0tJo0aaLExEQ98cQT9jFhYWFav369pkyZokWLFqlVq1Z65ZVXFBcX5+zhAwAAAABQbU6X7qlTp+rHH3/Un//8Z5WVlUmSvLy8NG3aNKWmpjo1V3VeEe7l5aXFixdr8eKLvzewTZs2+uCDDy45T0xMjPbu3etUPgAAAAAAfg+nS7fJZNIzzzyjxx9/XAcPHpS3t7ciIiLsl3IDAAAAAIBfXNZ7uiWpadOm6tmzZ01mAQAAAACgXnG6dJ8+fVpz585VZmamCgsLVVlZ6bD922+/rbFwAAAAAADUZU6X7vvvv1/bt2/XPffco5CQEJlMJiNyAQAAAABQ5zldujds2KD169erT58+RuQBAAAAAKDecPo93VdddZUCAwONyAIAAAAAQL3idOl+8sknNWPGDJ05c8aIPAAAAAAA1BtOX14+f/58HT16VMHBwWrbtq3c3d0dtn/++ec1Fg4AAAAAgLrM6dI9fPhwA2IAAAAAAFD/OF26Z86caUQOAAAAAADqHafv6QYAAAAAANXj9JnuiooKLVy4UG+99Zby8vJUVlbmsP3kyZM1Fg4AAAAAgLrM6TPds2bN0oIFC3TXXXepuLhYycnJuuOOO9SoUSOlpaUZEBEAAAAAgLrJ6dK9atUq/eMf/9Ajjzyixo0ba+TIkXrllVc0Y8YM7dq1y4iMAAAAAADUSU6XbovFoi5dukiSmjZtquLiYknSrbfeqvXr19dsOgAAAAAA6jCnS3erVq2Un58vSWrXrp02b94sSfr000/l6elZs+kAAAAAAKjDnC7dt99+uzIzMyVJDz30kB5//HFFRERo9OjRuu+++2o8IAAAAAAAdZXTTy+fO3eu/c933XWXWrduraysLEVERGjo0KE1Gg4AAAAAgLrM6dL9v6KjoxUdHV0TWQAAAAAAqFcuq3SfOHFCn3zyiQoLC1VZWemwbdKkSTUSDAAAAACAus7p0r1ixQo98MAD8vDwULNmzWQymezbTCYTpRsAAAAAgP+f06X78ccf14wZM5SamqpGjZx+DhsAAAAAAA2G0635zJkzGjFiBIUbAAAAAIDf4HRzHjt2rNasWWNEFgAAAAAA6hWnLy+fM2eObr31Vm3cuFFdunSRu7u7w/YFCxbUWDgAAAAAAOqyyyrdmzZtUocOHSSpyoPUAAAAAADAL5wu3fPnz9eyZcs0ZswYA+IAAAAAAFB/OH1Pt6enp/r06WNEFgAAAAAA6hWnS/fDDz+s559/3ogsAAAAAADUK05fXr57925t3bpV6enpuvbaa6s8SO3dd9+tsXAAAAAAANRlTpfugIAA3XHHHUZkAQAAAACgXnGqdJ87d0633HKLBg4cKLPZbFQmAAAAAADqBafu6W7cuLEmTJig0tJSo/IAAAAAAFBvOP0gtRtuuEF79+41IgsAAAAAAPWK0/d0//nPf9Yjjzyi48ePq0ePHmrSpInD9qioqBoLBwAAAABAXeZ06R4xYoQkadKkSfZ1JpNJNptNJpNJFRUVNZcOAAAAAIA6zOnSnZuba0QOAAAAAADqHadLd5s2bYzIAQAAAABAveN06Zako0eP6tlnn9XBgwclSZGRkXr44YfVrl27Gg0HAAAAAEBd5vTTyzdt2qTIyEjt3r1bUVFRioqKUnZ2tq699lplZGQYkREAAAAAgDrJ6TPdjz32mKZMmaK5c+dWWT9t2jTFxsbWWDgAAAAAAOoyp890Hzx4UGPHjq2y/r777tOBAwdqJBQAAAAAAPWB06W7RYsWysnJqbI+JydHQUFBNZEJAAAAAIB6wenLy8eNG6fx48fr22+/1Y033ihJ2rFjh5555hklJyfXeEAAAAAAAOoqp0v3448/Ll9fX82fP1+pqamSpJYtWyotLU2TJk2q8YAAAAAAANRV1bq8fN26dSovL5ckmUwmTZkyRcePH1dxcbGKi4t1/PhxPfzwwzKZTIaGBQAAAACgLqlW6b799ttVVFQkSXJzc1NhYaEkydfXV76+voaFAwAAAACgLqtW6W7RooV27dolSbLZbJzRBgAAAACgGqp1T/eECRM0bNgwmUwmmUwmmc3mi46tqKiosXAAAAAAANRl1SrdaWlpGjFihI4cOaLbbrtNy5cvV0BAgMHRAAAAAACo26r99PKOHTuqQ4cOSkxMVEJCgpo2bWpkLgAAAAAA6rxq3dN9ns1m06pVq5Sfn29UHgAAAAAA6g2nSnejRo0UERGhH3/80ag8AAAAAADUG06VbkmaO3eupk6dqq+++sqIPAAAAAAA1BvVvqf7vNGjR+vMmTPq2rWrPDw85O3t7bD95MmTNRYOAAAAAIC6zOnS/eyzzxoQAwAAAACA+sfp0p2YmGhEDgAAAAAA6h2n7+mWpKNHj2r69OkaOXKkCgsLJUkbNmzQ/v37azQcAAAAAAB1mdOle/v27erSpYuys7P17rvvqqSkRJL0xRdfaObMmU7N9dFHH2no0KFq2bKlTCaT1q5d67B9zJgxMplMDp/4+HiHMSdPntSoUaPk5+engIAAjR071p7pvC+//FJ/+MMf5OXlpdDQUM2bN8/ZwwYAAAAAwGlOl+7HHntMs2fPVkZGhjw8POzr+/Xrp127djk11+nTp9W1a1ctXrz4omPi4+OVn59v/7zxxhsO20eNGqX9+/crIyND6enp+uijjzR+/Hj7dqvVqoEDB6pNmzbas2eP/va3vyktLU0vv/yyU1kBAAAAAHCW0/d079u3T6+//nqV9UFBQfrhhx+cmmvQoEEaNGjQJcd4enrKbDZfcNvBgwe1ceNGffrpp7r++uslSc8//7wGDx6sv//972rZsqVWrVqlsrIyLVu2TB4eHrr22muVk5OjBQsWOJRzAAAAAABqmtNnugMCApSfn19l/d69e3X11VfXSKhf27Ztm4KCgtShQwc9+OCD+vHHH+3bsrKyFBAQYC/ckjRgwAA1atRI2dnZ9jF9+/Z1OCsfFxenw4cP66effrrgPktLS2W1Wh0+AAAAAAA4y+nSPWLECE2bNk0Wi0Umk0mVlZXasWOHHn30UY0ePbpGw8XHx+uf//ynMjMz9cwzz2j79u0aNGiQKioqJEkWi0VBQUEO32ncuLECAwNlsVjsY4KDgx3GnF8+P+Z/zZkzR/7+/vZPaGhojR4XAAAAAKBhcPry8qefflpJSUkKDQ1VRUWFIiMjVVFRobvvvlvTp0+v0XAjRoyw/7lLly6KiopSu3bttG3bNvXv379G9/VrqampSk5Oti9brVaKNwAAAADAaU6Xbg8PD/3jH//QjBkztG/fPpWUlKhbt26KiIgwIp+Da665Rs2bN9eRI0fUv39/mc1m+yvLzjt37pxOnjxpvw/cbDaroKDAYcz55YvdK+7p6SlPT08DjgAAAAAA0JBUu3RXVlbqb3/7m9atW6eysjL1799fM2fOlLe3t5H5HBw/flw//vijQkJCJEnR0dEqKirSnj171KNHD0nS1q1bVVlZqV69etnH/PWvf1V5ebnc3d0lSRkZGerQoYOuuuqqK5YdAAAAANDwVPue7qeeekp/+ctf1LRpU1199dVatGiRkpKSftfOS0pKlJOTo5ycHElSbm6ucnJylJeXp5KSEk2dOlW7du3Sd999p8zMTA0bNkzh4eGKi4uTJHXq1Enx8fEaN26cdu/erR07dmjixIkaMWKEWrZsKUm6++675eHhobFjx2r//v1avXq1Fi1a5HD5OAAAAAAARqh26f7nP/+pJUuWaNOmTVq7dq3ef/99rVq1SpWVlZe9888++0zdunVTt27dJEnJycnq1q2bZsyYITc3N3355Ze67bbb1L59e40dO1Y9evTQxx9/7HDp96pVq9SxY0f1799fgwcP1k033eTwDm5/f39t3rxZubm56tGjhx555BHNmDGD14UBAAAAAAxX7cvL8/LyNHjwYPvygAEDZDKZdOLECbVq1eqydh4TEyObzXbR7Zs2bfrNOQIDAy/43vBfi4qK0scff+x0PgAAAAAAfo9qn+k+d+6cvLy8HNa5u7urvLy8xkMBAAAAAFAfVPtMt81m05gxYxwu7T579qwmTJigJk2a2Ne9++67NZsQAAAAAIA6qtqlOzExscq6P/3pTzUaBgAAAACA+qTapXv58uVG5gAAAAAAoN6p9j3dAAAAAADAOZRuAAAAAAAMQukGAAAAAMAglG4AAAAAAAxC6QYAAAAAwCCUbgAAAAAADELpBgAAAADAIJRuAAAAAAAMQukGAAAAAMAglG4AAAAAAAxC6QYAAAAAwCCUbgAAAAAADELpBgAAAADAIJRuAAAAAAAMQukGAAAAAMAglG4AAAAAAAxC6QYAAAAAwCCUbgAAAAAADELpBgAAAADAIJRuAAAAAAAMQukGAAAAAMAglG4AAAAAAAxC6QYAAAAAwCCUbgAAAAAADELpBgAAAADAIJRuAAAAAAAMQukGAAAAAMAglG4AAAAAAAxC6QYAAAAAwCCUbgAAAAAADELpBgAAAADAIJRuAAAAAAAMQukGAAAAAMAglG4AAAAAAAxC6QYAAAAAwCCUbgAAAAAADELpBgAAAADAIJRuAAAAAAAMQukGAAAAAMAglG4AAAAAAAxC6QYAAAAAwCCUbgAAAAAADELpBgAAAADAIJRuAAAAAAAMQukGAAAAAMAglG4AAAAAAAxC6QYAAAAAwCCUbgAAAAAADELpBgAAAADAIJRuAAAAAAAMQukGAAAAAMAglG4AAAAAAAxC6QYAAAAAwCCUbgAAAAAADOLS0v3RRx9p6NChatmypUwmk9auXeuw3WazacaMGQoJCZG3t7cGDBigb775xmHMyZMnNWrUKPn5+SkgIEBjx45VSUmJw5gvv/xSf/jDH+Tl5aXQ0FDNmzfP6EMDAAAAAMC1pfv06dPq2rWrFi9efMHt8+bN03PPPaelS5cqOztbTZo0UVxcnM6ePWsfM2rUKO3fv18ZGRlKT0/XRx99pPHjx9u3W61WDRw4UG3atNGePXv0t7/9TWlpaXr55ZcNPz4AAAAAQMPW2JU7HzRokAYNGnTBbTabTc8++6ymT5+uYcOGSZL++c9/Kjg4WGvXrtWIESN08OBBbdy4UZ9++qmuv/56SdLzzz+vwYMH6+9//7tatmypVatWqaysTMuWLZOHh4euvfZa5eTkaMGCBQ7lHAAAAACAmlZr7+nOzc2VxWLRgAED7Ov8/f3Vq1cvZWVlSZKysrIUEBBgL9ySNGDAADVq1EjZ2dn2MX379pWHh4d9TFxcnA4fPqyffvrpCh0NAAAAAKAhcumZ7kuxWCySpODgYIf1wcHB9m0Wi0VBQUEO2xs3bqzAwECHMWFhYVXmOL/tqquuqrLv0tJSlZaW2petVuvvPBoAAAAAQENUa890u9KcOXPk7+9v/4SGhro6EgAAAACgDqq1pdtsNkuSCgoKHNYXFBTYt5nNZhUWFjpsP3funE6ePOkw5kJz/Hof/ys1NVXFxcX2z7Fjx37/AQEAAAAAGpxaW7rDwsJkNpuVmZlpX2e1WpWdna3o6GhJUnR0tIqKirRnzx77mK1bt6qyslK9evWyj/noo49UXl5uH5ORkaEOHTpc8NJySfL09JSfn5/DBwAAAAAAZ7m0dJeUlCgnJ0c5OTmSfnl4Wk5OjvLy8mQymTR58mTNnj1b69at0759+zR69Gi1bNlSw4cPlyR16tRJ8fHxGjdunHbv3q0dO3Zo4sSJGjFihFq2bClJuvvuu+Xh4aGxY8dq//79Wr16tRYtWqTk5GQXHTUAAAAAoKFw6YPUPvvsM91yyy325fNFODExUStWrFBKSopOnz6t8ePHq6ioSDfddJM2btwoLy8v+3dWrVqliRMnqn///mrUqJESEhL03HPP2bf7+/tr8+bNSkpKUo8ePdS8eXPNmDGD14UBAAAAAAxnstlsNleHqO2sVqv8/f1VXFxc6y81b/vYeldHAOy+87rb1REAB13CWrs6AuDgrTnnXB0BsNsas9jVEQAHSUv7uTrCJVW3J9bae7oBAAAAAKjrKN0AAAAAABiE0g0AAAAAgEEo3QAAAAAAGITSDQAAAACAQSjdAAAAAAAYhNINAAAAAIBBKN0AAAAAABiE0g0AAAAAgEEo3QAAAAAAGITSDQAAAACAQSjdAAAAAAAYhNINAAAAAIBBKN0AAAAAABiE0g0AAAAAgEEo3QAAAAAAGITSDQAAAACAQSjdAAAAAAAYhNINAAAAAIBBKN0AAAAAABiE0g0AAAAAgEEo3QAAAAAAGITSDQAAAACAQSjdAAAAAAAYhNINAAAAAIBBKN0AAAAAABiE0g0AAAAAgEEo3QAAAAAAGITSDQAAAACAQSjdAAAAAAAYhNINAAAAAIBBKN0AAAAAABiE0g0AAAAAgEEo3QAAAAAAGITSDQAAAACAQSjdAAAAAAAYhNINAAAAAIBBKN0AAAAAABiE0g0AAAAAgEEo3QAAAAAAGITSDQAAAACAQSjdAAAAAAAYhNINAAAAAIBBKN0AAAAAABiE0g0AAAAAgEEo3QAAAAAAGITSDQAAAACAQSjdAAAAAAAYhNINAAAAAIBBKN0AAAAAABiE0g0AAAAAgEEo3QAAAAAAGITSDQAAAACAQSjdAAAAAAAYhNINAAAAAIBBKN0AAAAAABiE0g0AAAAAgEEo3QAAAAAAGKRWl+60tDSZTCaHT8eOHe3bz549q6SkJDVr1kxNmzZVQkKCCgoKHObIy8vTkCFD5OPjo6CgIE2dOlXnzp270ocCAAAAAGiAGrs6wG+59tprtWXLFvty48b/L/KUKVO0fv16rVmzRv7+/po4caLuuOMO7dixQ5JUUVGhIUOGyGw2a+fOncrPz9fo0aPl7u6up59++oofCwAAAACgYan1pbtx48Yym81V1hcXF+vVV1/V66+/rn79+kmSli9frk6dOmnXrl3q3bu3Nm/erAMHDmjLli0KDg7WddddpyeffFLTpk1TWlqaPDw8rvThAAAAAAAakFp9ebkkffPNN2rZsqWuueYajRo1Snl5eZKkPXv2qLy8XAMGDLCP7dixo1q3bq2srCxJUlZWlrp06aLg4GD7mLi4OFmtVu3fv//KHggAAAAAoMGp1We6e/XqpRUrVqhDhw7Kz8/XrFmz9Ic//EFfffWVLBaLPDw8FBAQ4PCd4OBgWSwWSZLFYnEo3Oe3n992MaWlpSotLbUvW63WGjoiAAAAAEBDUqtL96BBg+x/joqKUq9evdSmTRu99dZb8vb2Nmy/c+bM0axZswybHwAAAADQMNT6y8t/LSAgQO3bt9eRI0dkNptVVlamoqIihzEFBQX2e8DNZnOVp5mfX77QfeLnpaamqri42P45duxYzR4IAAAAAKBBqFOlu6SkREePHlVISIh69Oghd3d3ZWZm2rcfPnxYeXl5io6OliRFR0dr3759KiwstI/JyMiQn5+fIiMjL7ofT09P+fn5OXwAAAAAAHBWrb68/NFHH9XQoUPVpk0bnThxQjNnzpSbm5tGjhwpf39/jR07VsnJyQoMDJSfn58eeughRUdHq3fv3pKkgQMHKjIyUvfcc4/mzZsni8Wi6dOnKykpSZ6eni4+OgAAAABAfVerS/fx48c1cuRI/fjjj2rRooVuuukm7dq1Sy1atJAkLVy4UI0aNVJCQoJKS0sVFxenJUuW2L/v5uam9PR0Pfjgg4qOjlaTJk2UmJioJ554wlWHBAAAAABoQGp16X7zzTcvud3Ly0uLFy/W4sWLLzqmTZs2+uCDD2o6GgAAAAAAv6lO3dMNAAAAAEBdQukGAAAAAMAglG4AAAAAAAxC6QYAAAAAwCCUbgAAAAAADELpBgAAAADAIJRuAAAAAAAMQukGAAAAAMAglG4AAAAAAAxC6QYAAAAAwCCUbgAAAAAADELpBgAAAADAIJRuAAAAAAAMQukGAAAAAMAglG4AAAAAAAxC6QYAAAAAwCCUbgAAAAAADELpBgAAAADAIJRuAAAAAAAMQukGAAAAAMAglG4AAAAAAAxC6QYAAAAAwCCUbgAAAAAADELpBgAAAADAIJRuAAAAAAAMQukGAAAAAMAglG4AAAAAAAxC6QYAAAAAwCCUbgAAAAAADELpBgAAAADAIJRuAAAAAAAMQukGAAAAAMAglG4AAAAAAAxC6QYAAAAAwCCUbgAAAAAADELpBgAAAADAIJRuAAAAAAAMQukGAAAAAMAglG4AAAAAAAxC6QYAAAAAwCCUbgAAAAAADELpBgAAAADAIJRuAAAAAAAMQukGAAAAAMAglG4AAAAAAAxC6QYAAAAAwCCUbgAAAAAADELpBgAAAADAIJRuAAAAAAAMQukGAAAAAMAglG4AAAAAAAxC6QYAAAAAwCCUbgAAAAAADELpBgAAAADAIJRuAAAAAAAMQukGAAAAAMAglG4AAAAAAAxC6QYAAAAAwCANqnQvXrxYbdu2lZeXl3r16qXdu3e7OhIAAAAAoB5rMKV79erVSk5O1syZM/X555+ra9euiouLU2FhoaujAQAAAADqqQZTuhcsWKBx48bp3nvvVWRkpJYuXSofHx8tW7bM1dEAAAAAAPVUgyjdZWVl2rNnjwYMGGBf16hRIw0YMEBZWVkuTAYAAAAAqM8auzrAlfDDDz+ooqJCwcHBDuuDg4N16NChKuNLS0tVWlpqXy4uLpYkWa1WY4PWgMrSM66OANhZTTZXRwAcVPxc4eoIgIOSCv6dRO3xc9lpV0cAHNT2/nU+n8126f/nbRCl21lz5szRrFmzqqwPDQ11QRqg7vJ3dQCgioOuDgA4uMHVAYBfO3KbqxMADqYud3WC6jl16pT8/S/+f74NonQ3b95cbm5uKigocFhfUFAgs9lcZXxqaqqSk5Pty5WVlTp58qSaNWsmk8lkeF6gPrBarQoNDdWxY8fk5+fn6jgAAABAjbLZbDp16pRatmx5yXENonR7eHioR48eyszM1PDhwyX9UqQzMzM1ceLEKuM9PT3l6enpsC4gIOAKJAXqHz8/P0o3AAAA6qVLneE+r0GUbklKTk5WYmKirr/+et1www169tlndfr0ad17772ujgYAAAAAqKcaTOm+66679P3332vGjBmyWCy67rrrtHHjxioPVwMAAAAAoKaYbL/1qDUAuAylpaWaM2eOUlNTq9yuAQAAADQUlG4AAAAAAAzSyNUBAAAAAACoryjdAAAAAAAYhNINAAAAAIBBKN1AAzJmzBiZTKYqn/j4eFdHk/RLvuHDh1dr7OLFi9W2bVt5eXmpV69e2r17t7HhAAAAgMtA6QYamPj4eOXn5zt83njjDZdmqqioUGVlZbXHr169WsnJyZo5c6Y+//xzde3aVXFxcSosLDQwJQAAAOA8SjfQwHh6espsNjt8rrrqKknStm3b5OHhoY8//tg+ft68eQoKClJBQYEkKSYmRhMnTtTEiRPl7++v5s2b6/HHH9evX4RQWlqqRx99VFdffbWaNGmiXr16adu2bfbtK1asUEBAgNatW6fIyEh5enrqvvvu08qVK/Xvf//bfgb+19/5tQULFmjcuHG69957FRkZqaVLl8rHx0fLli2r+R8MAAAA+B0auzoAgNojJiZGkydP1j333KMvvvhC3377rR5//HGtWbNGwcHB9nErV67U2LFjtXv3bn322WcaP368WrdurXHjxkmSJk6cqAMHDujNN99Uy5Yt9d577yk+Pl779u1TRESEJOnMmTN65pln9Morr6hZs2YKCQnRzz//LKvVquXLl0uSAgMDq2QsKyvTnj17lJqaal/XqFEjDRgwQFlZWUb+PAAAAIDTKN1AA5Oenq6mTZs6rPvLX/6iv/zlL5Kk2bNnKyMjQ+PHj9dXX32lxMRE3XbbbQ7jQ0NDtXDhQplMJnXo0EH79u3TwoULNW7cOOXl5Wn58uXKy8tTy5YtJUmPPvqoNm7cqOXLl+vpp5+WJJWXl2vJkiXq2rWrfV5vb2+VlpbKbDZfNP8PP/ygiooKh78EkKTg4GAdOnTo8n8YAAAAwACUbqCBueWWW/Tiiy86rPv1GWUPDw+tWrVKUVFRatOmjRYuXFhljt69e8tkMtmXo6OjNX/+fFVUVGjfvn2qqKhQ+/btHb5TWlqqZs2aOewnKiqqpg4LAAAAqJUo3UAD06RJE4WHh19yzM6dOyVJJ0+e1MmTJ9WkSZNqz19SUiI3Nzft2bNHbm5uDtt+fYbd29vbobhXV/PmzeXm5ma/x/y8goKCS54hBwAAAFyBB6kBcHD06FFNmTJF//jHP9SrVy8lJiZWebJ4dna2w/KuXbsUEREhNzc3devWTRUVFSosLFR4eLjD57dKsYeHhyoqKn5zTI8ePZSZmWlfV1lZqczMTEVHRzt5tAAAAICxKN1AA1NaWiqLxeLw+eGHHyT98uquP/3pT4qLi9O9996r5cuX68svv9T8+fMd5sjLy1NycrIOHz6sN954Q88//7wefvhhSVL79u01atQojR49Wu+++65yc3O1e/duzZkzR+vXr79ktrZt2+rLL7/U4cOH9cMPP6i8vPyC45KTk/WPf/xDK1eu1MGDB/Xggw/q9OnTuvfee2vgFwIAAABqDpeXAw3Mxo0bFRIS4rCuQ4cOOnTokJ566in95z//UXp6uiQpJCREL7/8skaOHKmBAwfaH3o2evRo/fzzz7rhhhvk5uamhx9+WOPHj7fPt3z5cs2ePVuPPPKI/vvf/6p58+bq3bu3br311ktmGzdunLZt26brr79eJSUl+vDDDxUTE1Nl3F133aXvv/9eM2bMkMVi0XXXXaeNGzdWebgaAAAA4Gom269frgsAvyEmJkbXXXednn32WVdHAQAAAGo9Li8HAAAAAMAglG4AAAAAAAzC5eUAAAAAABiEM90AAAAAABiE0g0AAAAAgEEo3QAAAAAAGITSDQAAAACAQSjdAAAAAAAYhNINAADqjTFjxmj48OGujgEAgB2lGwCAahozZoxMJpNMJpM8PDwUHh6uJ554QufOnXN1tMtmMpm0du1aw+bPzc3V3XffrZYtW8rLy0utWrXSsGHDdOjQoWrPcaEi/d1338lkMiknJ8dh/aJFi7RixYrfHxwAgBrS2NUBAACoS+Lj47V8+XKVlpbqgw8+UFJSktzd3ZWamur0XBUVFTKZTGrUqO7/HXh5ebnc3d2rrIuNjVWHDh307rvvKiQkRMePH9eGDRtUVFRkSA5/f39D5gUA4HLV/f/KAwBwBXl6espsNqtNmzZ68MEHNWDAAK1bt06StGDBAnXp0kVNmjRRaGio/vznP6ukpMT+3RUrViggIEDr1q1TZGSkPD09lZeXp08//VSxsbFq3ry5/P39dfPNN+vzzz932K/JZNJLL72kW2+9VT4+PurUqZOysrJ05MgRxcTEqEmTJrrxxht19OhRh+/9+9//Vvfu3eXl5aVrrrlGs2bNsp+Zb9u2rSTp9ttvl8lksi//1vfO53nxxRd12223qUmTJnrqqaeq/Fb79+/X0aNHtWTJEvXu3Vtt2rRRnz59NHv2bPXu3ds+7tixY/rjH/+ogIAABQYGatiwYfruu+8kSWlpaVq5cqX+/e9/268y2LZtm8LCwiRJ3bp1k8lkUkxMjKSqZ8VjYmI0adIkpaSkKDAwUGazWWlpaQ45Dx06pJtuukleXl6KjIzUli1bHK4AKCsr08SJExUSEiIvLy+1adNGc+bMucC/HQAAVEXpBgDgd/D29lZZWZkkqVGjRnruuee0f/9+rVy5Ulu3blVKSorD+DNnzuiZZ57RK6+8ov379ysoKEinTp1SYmKiPvnkE+3atUsREREaPHiwTp065fDdJ598UqNHj1ZOTo46duyou+++Ww888IBSU1P12WefyWazaeLEifbxH3/8sUaPHq2HH35YBw4c0EsvvaQVK1bYC/Knn34qSVq+fLny8/Pty7/1vfPS0tJ0++23a9++fbrvvvuq/DYtWrRQo0aN9Pbbb6uiouKCv195ebni4uLk6+urjz/+WDt27FDTpk0VHx+vsrIyPfroo/rjH/+o+Ph45efnKz8/XzfeeKN2794tSdqyZYvy8/P17rvvXvSf0cqVK9WkSRNlZ2dr3rx5euKJJ5SRkSHpl6sNhg8fLh8fH2VnZ+vll1/WX//6V4fvP/fcc1q3bp3eeustHT58WKtWrXL4CwoAAC7JBgAAqiUxMdE2bNgwm81ms1VWVtoyMjJsnp6etkcfffSC49esWWNr1qyZfXn58uU2SbacnJxL7qeiosLm6+tre//99+3rJNmmT59uX87KyrJJsr366qv2dW+88YbNy8vLvty/f3/b008/7TD3v/71L1tISIjDvO+9957DmOp+b/LkyZc8DpvNZnvhhRdsPj4+Nl9fX9stt9xie+KJJ2xHjx51mLdDhw62yspK+7rS0lKbt7e3bdOmTTabzfF3Py83N9cmybZ3716H9f879uabb7bddNNNDmN69uxpmzZtms1ms9k2bNhga9y4sS0/P9++PSMjw+F3eeihh2z9+vVzyAgAQHVxphsAACekp6eradOm8vLy0qBBg3TXXXfZL1fesmWL+vfvr6uvvlq+vr6655579OOPP+rMmTP273t4eCgqKsphzoKCAo0bN04RERHy9/eXn5+fSkpKlJeX5zDu198LDg6WJHXp0sVh3dmzZ2W1WiVJX3zxhZ544gk1bdrU/hk3bpzy8/MdMv2v6n7v+uuv/83fKykpSRaLRatWrVJ0dLTWrFmja6+91n6m+YsvvtCRI0fk6+tr31dgYKDOnj1b5VL5y/W/v3dISIgKCwslSYcPH1ZoaKjMZrN9+w033OAwfsyYMcrJyVGHDh00adIkbd68uUZyAQAaBh6kBgCAE2655Ra9+OKL8vDwUMuWLdW48S//Kf3uu+9066236sEHH9RTTz2lwMBAffLJJxo7dqzKysrk4+Mj6ZfL0U0mk8OciYmJ+vHHH7Vo0SK1adNGnp6eio6Otl+2ft6vH1R2fo4LrausrJQklZSUaNasWbrjjjuqHIeXl9dFj7G632vSpMlF5/g1X19fDR06VEOHDtXs2bMVFxen2bNnKzY2ViUlJerRo4dWrVpV5XstWrSo1vy/5X8f8GYymey/UXV0795dubm52rBhg7Zs2aI//vGPGjBggN5+++0ayQcAqN8o3QAAOKFJkyYKDw+vsn7Pnj2qrKzU/Pnz7U8jf+utt6o1544dO7RkyRINHjxY0i8PFvvhhx9+d9bu3bvr8OHDF8x7nru7e5X7ravzvctlMpnUsWNH7dy5076v1atXKygoSH5+fhf8joeHR5WMHh4eknTRe8Wrq0OHDjp27JgKCgrsVw+cv7f91/z8/HTXXXfprrvu0p133qn4+HidPHlSgYGBv2v/AID6j8vLAQCoAeHh4SovL9fzzz+vb7/9Vv/617+0dOnSan03IiJC//rXv3Tw4EFlZ2dr1KhR8vb2/t2ZZsyYoX/+85+aNWuW9u/fr4MHD+rNN9/U9OnT7WPatm2rzMxMWSwW/fTTT9X+XnXk5ORo2LBhevvtt3XgwAEdOXJEr776qpYtW6Zhw4ZJkkaNGqXmzZtr2LBh+vjjj5Wbm6tt27Zp0qRJOn78uD3jl19+qcOHD+uHH35QeXm5goKC5O3trY0bN6qgoEDFxcWX9RvFxsaqXbt2SkxM1JdffqkdO3bYj/P8lQMLFizQG2+8oUOHDunrr7/WmjVrZDabFRAQcFn7BAA0LJRuAABqQNeuXbVgwQI988wz6ty5s1atWlXt10q9+uqr+umnn9S9e3fdc889mjRpkoKCgn53pri4OKWnp2vz5s3q2bOnevfurYULF6pNmzb2MfPnz1dGRoZCQ0PVrVu3an+vOlq1aqW2bdtq1qxZ6tWrl7p3765FixZp1qxZ9ieE+/j46KOPPlLr1q11xx13qFOnTho7dqzOnj1rP/M9btw4dejQQddff71atGihHTt2qHHjxnruuef00ksvqWXLlvYS7yw3NzetXbtWJSUl6tmzp+6//357tvOX0vv6+mrevHm6/vrr1bNnT3333Xf64IMP6sX71QEAxjPZbDabq0MAAADUFjt27NBNN92kI0eOqF27dq6OAwCo4yjdAACgQXvvvffUtGlTRURE6MiRI3r44Yd11VVX6ZNPPnF1NABAPcCD1AAAQIN26tQpTZs2TXl5eWrevLkGDBig+fPnuzoWAKCe4Ew3AAAAAAAG4QkgAAAAAAAYhNINAAAAAIBBKN0AAAAAABiE0g0AAAAAgEEo3QAAAAAAGITSDQAAAACAQSjdAAAAAAAYhNINAAAAAIBBKN0AAAAAABjk/wNjJOb76A9eDQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt  \n",
    "import numpy as np  \n",
    "  \n",
    "# 假设的数据集  \n",
    "# 模型大小: ['330M', '2B', '10B']  \n",
    "# 参数设置: ['Expert 0', 'Lester et al.', 'P-tuning']  \n",
    "# 每个参数设置在不同模型大小上的性能分数（这里用随机数模拟）  \n",
    "data = {  \n",
    "    'Inc 0': {'Expert 0': 2000},   \n",
    "    'Inc 1': {'Expert 0': 2174, 'Expert 1': 1826},   \n",
    "    'Inc 2': {'Expert 0': 1804, 'Expert 1': 2389, 'Expert 2': 1807},   \n",
    "    'Inc 3': {'Expert 0': 2013, 'Expert 1': 2977, 'Expert 2': 2400, 'Expert 3': 610},   \n",
    "    'Inc 4': {'Expert 0': 1483, 'Expert 1': 3631, 'Expert 2': 2091, 'Expert 3': 1474, 'Expert 4': 1321}  \n",
    "}  \n",
    "  \n",
    "# 提取所有模型大小和参数设置  \n",
    "model_sizes = list(data.keys())  \n",
    "param_settings = list(data[model_sizes[0]].keys())  \n",
    "  \n",
    "# 设置图形大小  \n",
    "plt.figure(figsize=(10, 6))  \n",
    "  \n",
    "# 为每个模型大小绘制一个柱状图组  \n",
    "index = np.arange(len(param_settings))  # 参数设置的索引  \n",
    "bar_width = 0.2  # 柱状图的宽度  \n",
    "  \n",
    "for i, model_size in enumerate(model_sizes):  \n",
    "    plt.bar(index + i * bar_width, list(data[model_size].values()), bar_width,  \n",
    "            label=model_size, align='center')  \n",
    "  \n",
    "# 添加图例  \n",
    "plt.xlabel('Parameter Settings')  \n",
    "plt.ylabel('Performance Score')  \n",
    "plt.xticks(index + bar_width / 2, param_settings)  \n",
    "plt.legend()  \n",
    "  \n",
    "# 显示图形  \n",
    "plt.tight_layout()  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt  \n",
    "  \n",
    "# 假设的性能数据  \n",
    "models = ['330M', '2B', '10B']  \n",
    "scores_fine_tuning = [72, 75, 78]  # 示例数据  \n",
    "scores_lester_et_al = [70, 74, 77]  # 示例数据  \n",
    "scores_p_tuning = [73, 76, 79]  # 示例数据  \n",
    "  \n",
    "# 设置图形大小  \n",
    "plt.figure(figsize=(10, 6))  \n",
    "  \n",
    "# 绘制柱状图  \n",
    "index = range(len(models))  \n",
    "bar_width = 0.25  \n",
    "  \n",
    "plt.bar(index, scores_fine_tuning, bar_width, label='Expert 0', color='red')  \n",
    "plt.bar([p + bar_width for p in index], scores_lester_et_al, bar_width, label='Lester et al.', color='blue')  \n",
    "plt.bar([p + bar_width * 2 for p in index], scores_p_tuning, bar_width, label='P-tuning', color='orange')  \n",
    "  \n",
    "# 添加x轴和y轴标签  \n",
    "plt.xlabel('Model Size')  \n",
    "plt.ylabel('Performance Score')  \n",
    "plt.xticks([r + bar_width for r in range(len(models))], models)  \n",
    "plt.title('Performance Comparison Across Different Model Sizes and Tuning Methods')  \n",
    "  \n",
    "# 添加图例  \n",
    "plt.legend()  \n",
    "  \n",
    "# 显示图形  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt  \n",
    "  \n",
    "# 数据  \n",
    "labels = ['Inc 0', 'Inc 1', 'Inc 2', 'Inc 3', 'Inc 4']  \n",
    "# scores = [[2000], [2174, 1826], [1804, 2389, 1807], [2013, 2977, 2400, 610], [1483, 3631, 2091, 1474, 1321]]\n",
    "scores = [[2000],[2031,1969],[1309,2834,1857],[1443,3171,2076,1310],[1524,3988,1934,1992,562]]  \n",
    "  \n",
    "# 设置图形和子图（这里其实只有一个子图）  \n",
    "fig, ax = plt.subplots()  \n",
    "  \n",
    "# 计算最大的子列表长度，以便设置x轴的刻度  \n",
    "max_length = max(len(s) for s in scores)  \n",
    "  \n",
    "# 绘制柱状图  \n",
    "x = range(len(labels))  # 横轴刻度，对应labels  \n",
    "bar_width = 0.8 / max_length  # 计算柱状图的宽度，以适应所有柱子  \n",
    "  \n",
    "for i, score_group in enumerate(scores):  \n",
    "    # 对于每个Inc，绘制相应数量的柱子  \n",
    "    for j, score in enumerate(score_group):  \n",
    "        # 计算当前柱子的x位置和宽度  \n",
    "        x_pos = x[i] - (max_length - len(score_group)) * bar_width / 2 + j * bar_width  \n",
    "        ax.bar(x_pos, score, bar_width, label=f'Expert {j}')  \n",
    "  \n",
    "# 设置x轴刻度标签  \n",
    "ax.set_xticks(x)  \n",
    "ax.set_xticklabels(labels)  \n",
    "  \n",
    "# 添加图例（如果多个柱子共享一个标签，则可能需要根据需要调整）  \n",
    "handles, labels = ax.get_legend_handles_labels()  \n",
    "by_label = dict(zip(labels, handles))  \n",
    "ax.legend(by_label.values(), by_label.keys(), loc='upper left', bbox_to_anchor=(1.05, 1), borderaxespad=0.)  \n",
    "  \n",
    "# 显示图形  \n",
    "plt.tight_layout()  \n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "torch_hwzhao",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
